Source: http://www.wias-berlin.de/research/rts/Inference/?lang=1
Timestamp: 2019-04-22 12:36:54+00:00

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The term statistical inference summerizes methods to extract information from observed data in order to characterize properties in populations. This involves statistical modeling, estimation and uncertainty assessment of parameters, testing of hypothesis.
Successful statistical modeling and data analysis often requires the use of smoothing procedures that can adapt to the unknown underlying structure. This includes an intrinsic detection of edges or discontinuities in imaging or signal processing as well as non-stationarities in time series. Established parametric and nonparametric methods often provide results that do not sufficiently meet demands from applications. The general principle of structural adaptation forms the basis of most of the new smoothing algorithms developed at WIAS. This approach enables an efficient description and analysis of complex discontinuous models. Within the recent years a series of innovative approaches and algorithms has been developed. Examples are adaptive weights smoothing (AWS) (Polzehl & Spokoiny 2000), , propagation separation methods (PS), (Polzehl & Spokoiny 2006, Becker & Mathé 2013), Pointwise Adaptive Methods (PA), (Spokoiny 1998, Polzehl & Spokoiny 2003, Spokoiny & Vial 2009), local model selection (LMS), stagewise aggregation (SA) and (Belomestny & Spokoiny 2007) local change point detection (LCP) (Mercurio & Spokoiny 2004, Spokoiny 2009). All these procedures are based on a new local parametric approach and have been justified within a series of publications on theoretical results. In this context important problems and questions in the field of modern parametric and nonparametric statistics have been solved. This includes the problem of automatic selection of smoothing parameters in case of qualitative structural assumptions. Structural adaptive methods have been developed and investigated for several classes of statistical models. Apart from regression this especially includes nonstationary time series and volatility models but also adaptive classification methods and models for extremes. Strong methodological connections exist to adaptive methods for dimension reduction and to multiscale methods. Several algorithms have been implemented as contributed packages ( adaptive weights smoothing aws, adaptive image processing adimpro, functional magnetic resonance imaging fmri and diffusion weighted magnetic resonance imaging dti) (Image processing/Neuroimaging Packages) of the R Environment for Statistical Computing .
Smoothing of a 3D MR-image using AWS with maximum bandwidth 5, 10 and 20 times voxelsize, 3 voxel are marked in blue, red and green.
For applications in neuroimaging see Computational neuroscience and medical imaging.
Software description can be found at a separate page.
The problem of dimension reduction arises in various forms with modeling or analysis of complex, usually high-dimensional data structures. Examples are relations between variables in regression models where single- or multi-index models (Hristache et al. 2001, Dalalyan et al. 2008) allow for a more efficient description and methods that infer on linear or nonlinear subspaces that contain most of the relevant information on underlying structures. Such models and corresponding procedures are often a prerequisite for an efficient statistical modeling and data analysis. Classical procedures like principal component analysis (PCA) or independent component analysis (ICA) are based on strict assumptions and are of limited use in this context. Based on the principle of structural adaptation novel methods and procedures for dimension reduction have been developed at WIAS. The proposed dimension reduction methods for regression can be interpreted as nonlinear PCA. These methods allow to efficiently estimate a dimension reduction space after a finite number of iterations. Non-Gaussian componet analysis (NGCA) (Blanchard et al. 2006 , Diederichs et al. 2013) provides a new promising approach to the analysis of high-dimensional data structures. It is based on the assumption that structure lives in low-dimensional subspaces and projections onto these subspaces are characterized by non-Gaussianity. This assumption is then used to identify these informative subspaces.
The more questions you ask, the more wrong answers you are expected to receive - even if every single source of your information is quite trustworthy. We consider cases in which the sources of information are data, and the questions are formalized by statistical hypothesis-alternative pairs. From the mathematical point of view, this leads to multiple test problems. Criteria and methods (multiple tests) are developed which ensure that with high probability not too many wrong decisions are made, even if many hypotheses are of interest under the scope of one and the same statistical model, i. e., regarding one and the same dataset. High-throughput technologies in different fields of modern life sciences have led to massive multiplicity and given rise to multiple test problems with more hypotheses than observations. Driven by these developments, also new statistical paradigms have arisen. In particular, it is fair to say that a new era of multiple testing began when Yoav Benjamini and Yosef Hochberg formally introduced the false discovery rate (FDR) and the linear step-up test for FDR control in 1995. At WIAS, FDR methodology and multivariate multiple tests are developed for temporally, spatially, or spatio-temporally dependent data. Furthermore, multiple test theory can also be used in related fields of simultaneous statistical inference and statistical learning, for example in the context of binary classification and model selection. Our contributions to simultaneous statistical inference are made with particular emphasis on life science applications in (behavioral) genetics, epigenetics, brain-computer interfacing, functional magnetic resonance imaging, and neuroeconomics. Main publications: Blanchard & Roquain 2009, Blanchard et al. 2010, Dickhaus 2013, Dickhaus et al 2013, Bodnar & Dickhaus 2017, Schildknecht et al. 2015, Schildknecht et al. 2016.
H.-G. Bartel, H.-J. Mucha, Chapter 2: Incomparability/Inequality Measures and Clustering, in: Partial Order Concepts in Applied Sciences, M. Fattore, R. Brüggemann, eds., Springer International Publishing, New York, 2017, pp. 21--34, (Chapter Published).
V. Spokoiny, Th. Dickhaus, Basics of Modern Mathematical Statistics, 18 of Springer Texts in Statistics, Springer, Berlin et al., 2015, 296 pages, (Monograph Published).
H.-G. Bartel, H.-J. Mucha, Chapter 3: Measures of Incomparability and of Inequality and Their Applications, in: Multi-indicator Systems and Modelling in Partial Order, R. Brüggemann, L. Carlsen, J. Wittmann, eds., Springer, New York et al., 2014, pp. 47--67, (Chapter Published).
TH. Dickhaus, Simultaneous Statistical Inference, Springer, Berlin et al., 2014, 180 pages, (Monograph Published).
A. Lejay, P. Pigato, Statistical estimation of the oscillating Brownian motion, Bernoulli. Official Journal of the Bernoulli Society for Mathematical Statistics and Probability, 24 (2018), pp. 3568--3602, DOI 10.3150/17-BEJ969 .
We study the asymptotic behavior of estimators of a two-valued, discontinuous diffusion coefficient in a Stochastic Differential Equation, called an Oscillating Brownian Motion. Using the relation of the latter process with the Skew Brownian Motion, we propose two natural consistent estimators, which are variants of the integrated volatility estimator and take the occupation times into account. We show the stable convergence of the renormalized errors? estimations toward some Gaussian mixture, possibly corrected by a term that depends on the local time. These limits stem from the lack of ergodicity as well as the behavior of the local time at zero of the process. We test both estimators on simulated processes, finding a complete agreement with the theoretical predictions.
Summary: In this paper, we prove new complexity bounds for methods of convex optimization based only on computation of the function value. The search directions of our schemes are normally distributed random Gaussian vectors. It appears that such methods usually need at most nn times more iterations than the standard gradient methods, where nn is the dimension of the space of variables. This conclusion is true for both nonsmooth and smooth problems. For the latter class, we present also an accelerated scheme with the expected rate of convergence O(n2k2)O(n2k2), where kk is the iteration counter. For stochastic optimization, we propose a zero-order scheme and justify its expected rate of convergence O(nk1/2)O(nk1/2). We give also some bounds for the rate of convergence of the random gradient-free methods to stationary points of nonconvex functions, for both smooth and nonsmooth cases. Our theoretical results are supported by preliminary computational experiments.
A. Kalinina, A. Suvorikova, V. Spokoiny, M. Gelfand, Detection of homologous recombination in closely related strains, Journal of Bioinformatics and Computational Biology, 14 (2016), pp. 1641001/1--1641001/12.
A. Andresen, V. Spokoiny, Convergence for an alternation maximization procedure, Journal of Machine Learning Research (JMLR). MIT Press, Cambridge, MA. English, English abstracts., 17 (2016), pp. 1--53.
A. Gasnikov, P. Dvurechensky, V. Spokoiny, P. Stetsyuk, A. Suvorikova, Superposition of the balancing algorithm and the universal gradient method for search of the regularized Wasserstein barycenter and equilibria in multistage transport models, Proceedings of Moscow Institute of Physics and Technology, 8 (2016), pp. 5--24.
CH. Bayer, A. Moraes, R. Tempone, P. Villanova, An efficient forward-reverse expectation-maximization algorithm for statistical inference in stochastic reaction networks, Stochastic Analysis and Applications, 34 (2016), pp. 193--231.
We consider non-degenerate centro-affine hypersurface immersions in Rn whose cubic form is parallel with respect to the Levi-Civita connection of the affine metric. There exists a bijective correspondence between homothetic families of proper affine hyperspheres with center in the origin and with parallel cubic form, and Köchers conic ? -domains, which are the maximal connected sets consisting of invertible elements in a real semi-simple Jordan algebra. Every level surface of the ? function in an ? -domain is an affine complete, Euclidean complete proper affine hypersphere with parallel cubic form and with center in the origin. On the other hand, every proper affine hypersphere with parallel cubic form and with center in the origin can be represented as such a level surface. We provide a complete classification of proper affine hyperspheres with parallel cubic form based on the classification of semi-simple real Jordan algebras. Centro-affine hypersurface immersions with parallel cubic form are related to the wider class of real unital Jordan algebras. Every such immersion can be extended to an affine complete one, whose conic hull is the connected component of the unit element in the set of invertible elements in a real unital Jordan algebra. Our approach can be used to study also other classes of hypersurfaces with parallel cubic form.
Epigenetic research leads to complex data structures. Since parametric model assumptions for the distribution of epigenetic data are hard to verify we introduce in the present work a nonparametric statistical framework for two-group comparisons. Furthermore, epigenetic analyses are often performed at various genetic loci simultaneously. Hence, in order to be able to draw valid conclusions for specific loci, an appropriate multiple testing correction is necessary. Finally, with technologies available for the simultaneous assessment of many interrelated biological parameters (such as gene arrays), statistical approaches also need to deal with a possibly unknown dependency structure in the data. Our statistical approach to the nonparametric comparison of two samples with independent multivariate observables is based on recently developed multivariate multiple permutation tests. We adapt their theory in order to cope with families of hypotheses regarding relative effects. Our results indicate that the multivariate multiple permutation test keeps the pre-assigned type I error level for the global null hypothesis. In combination with the closure principle, the family-wise error rate for the simultaneous test of the corresponding locus/parameter-specific null hypotheses can be controlled. In applications we demonstrate that group differences in epigenetic data can be detected reliably with our methodology.
We provide necessary and sufficient conditions for the validity of the inequality of Simes (1986) in models with elliptical dependencies. Necessary conditions are presented in terms of sufficient conditions for the reverse Simes inequality. One application of our main results concerns the problem of model misspecification, in particular the case that the assumption of Gaussianity of test statistics is violated. Since our sufficient conditions require non-negativity of correlation coefficients between test statistics, we also develop exact tests for vectors of correlation coefficients.
We are considered with three different types of multivariate chi-square distributions. Their members play important roles as limiting distributions of vectors of test statistics in several applications of multiple hypotheses testing. We explain these applications and provide formulas for computing multiplicity-adjusted $p$-values under the respective global hypothesis.
A multiplier bootstrap procedure for construction of likelihood-based confidence sets is considered for finite samples and possible model misspecification. Theoretical results justify the bootstrap consistency for small or moderate sample size and allow to control the impact of the parameter dimension: the bootstrap approximation works if the ratio of cube of the parameter dimension to the sample size is small. The main result about bootstrap consistency continues to apply even if the underlying parametric model is misspecified under the so called Small Modeling Bias condition. In the case when the true model deviates significantly from the considered parametric family, the bootstrap procedure is still applicable but it becomes a bit conservative: the size of the constructed confidence sets is increased by the modeling bias. We illustrate the results with numerical examples of misspecified constant and logistic regressions.
A. Andresen, V. Spokoiny, Critical dimension in profile semiparametric estimation, Electronic Journal of Statistics, 8 (2014), pp. 3077--3125.
D. Belomestny, V. Spokoiny, Concentration inequalities for smooth random fields, Theory of Probability and its Applications, 58 (2014), pp. 314--323.
In this note we derive a sharp concentration inequality for the supremum of a smooth random field over a finite dimensional set. It is shown that this supremum can be bounded with high probability by the value of the field at some deterministic point plus an intrinsic dimension of the optimisation problem. As an application we prove the exponential inequality for a function of the maximal eigenvalue of a random matrix is proved.
G. Blanchard, Th. Dickhaus, E. Roquain, F. Villers, On least favorable configurations for step-up-down tests, Statistica Sinica, 24 (2014), pp. 1--23.
T. Bodnar, Th. Dickhaus, False discovery rate control under Archimedean copula, Electronic Journal of Statistics, 8 (2014), pp. 2207--2241.
The Propagation-Separation approach is an iterative procedure for pointwise estimation of local constant and local polynomial functions. The estimator is defined as a weighted mean of the observations with data-driven weights. Within homogeneous regions it ensures a similar behavior as non-adaptive smoothing (propagation), while avoiding smoothing among distinct regions (separation). In order to enable a proof of stability of estimates, the authors of the original study introduced an additional memory step aggregating the estimators of the successive iteration steps. Here, we study theoretical properties of the simplified algorithm, where the memory step is omitted. In particular, we introduce a new strategy for the choice of the adaptation parameter yielding propagation and stability for local constant functions with sharp discontinuities.
H.-G. Bartel, H.-J. Mucha, Finding incomparable pairs of subsets by using formal concept analysis, Statistica & Applicazioni, Special Issue (2011), pp. 61--79.
A common setting for novelty detection assumes that labeled examples from the nominal class are available, but that labeled examples of novelties are unavailable. The standard (inductive) approach is to declare novelties where the nominal density is low, which reduces the problem to density level set estimation. In this paper, we consider the setting where an unlabeled and possibly contaminated sample is also available at learning time. We argue that novelty detection in this semi-supervised setting is naturally solved by a general reduction to a binary classification problem. In particular, a detector with a desired false positive rate can be achieved through a reduction to Neyman-Pearson classification. Unlike the inductive approach, semi-supervised novelty detection (SSND) yields detectors that are optimal (e.g., statistically consistent) regardless of the distribution on novelties. Therefore, in novelty detection, unlabeled data have a substantial impact on the theoretical properties of the decision rule. We validate the practical utility of SSND with an extensive experimental study. We also show that SSND provides distribution-free, learning-theoretic solutions to two well known problems in hypothesis testing. First, our results provide a general solution to the general two-sample problem, that is, the problem of determining whether two random samples arise from the same distribution. Second, a specialization of SSND coincides with the standard $p$-value approach to multiple testing under the so-called random effects model. Unlike standard rejection regions based on thresholded $p$-values, the general SSND framework allows for adaptation to arbitrary alternative distributions.
H.-G. Bartel, J. Dolata, H.-J. Mucha, Klassifikation von 613 Proben als Referenzen für die Herstellungsprovenienzen römischer Baukeramik im nördlichen Obergermanien, Mainzer Archäologische Zeitschrift, 8 (2009), pp. 51--76.
P. Čížek, W. Härdle, V. Spokoiny, Adaptive pointwise estimation in time-inhomogeneous conditional heteroscedasticity models, The Econometrics Journal, 12 (2009), pp. 248--271.
E. Giacomini, W. Härdle, V. Krätschmer, Dynamic semiparametric factor models in risk neutral density estimation, AStA. Advances in Statistical Analysis. A Journal of the German Statistical Society, 93 (2009), pp. 387--402.
E. Giacomini, W. Härdle, V. Spokoiny, Inhomogeneous dependency modelling with time varying copulae, Journal of Business & Economic Statistics, 27 (2009), pp. 224--234.
Y. Golubev, V. Spokoiny, Exponential bounds for minimum contrast estimators, Electronic Journal of Statistics, 3 (2009), pp. 712--746.
A. Schwaighöfer, T. Schröter, S. Mika, G. Blanchard, How wrong can we get? A review of machine learning approaches and error bars, Combinatorial Chemistry & High Throughput Screening, 12 (2009), pp. 453--468.
V. Spokoiny, Multiscale local change point detection with applications to Value-at-Risk, The Annals of Statistics, 37 (2009), pp. 1405--1436.
This paper offers a new procedure for nonparametric estimation and forecasting of time series with applications to volatility modeling for financial data. The approach is based on the assumption of local homogeneity: for every time point there exists a historical emphinterval of homogeneity, in which the volatility parameter can be well approximated by a constant. The procedure recovers this interval from the data using the local change point (LCP) analysis. Afterwards the estimate of the volatility can be simply obtained by local averaging. The approach carefully addresses the question of choosing the tuning parameters of the procedure using the so called “propagation” condition. The main result claims a new “oracle” inequality in terms of the modeling bias which measures the quality of the local constant approximation. This result yields the optimal rate of estimation for smooth and piecewise constant volatility functions. Then the new procedure is applied to some data sets and a comparison with a standard GARCH model is also provided. Finally we discuss applications of the new method to the Value at Risk problem. The numerical results demonstrate a very reasonable performance of the new method.
H.-G. Bartel, H.-J. Mucha, J. Dolata, Über Identifikationsmethoden, dargestellt am Beispiel römischer Baukeramik, Berliner Beiträge zur Archäometrie, 21 (2008), pp. 115--132.
A. Dalalyan, A. Juditsky, V. Spokoiny, A new algorithm for estimating the effective dimension-reduction subspace, Journal of Machine Learning Research (JMLR). MIT Press, Cambridge, MA. English, English abstracts., 9 (2008), pp. 1647--1678.
I.G. Grama, V. Spokoiny, Statistics of extremes by oracle estimation, The Annals of Statistics, 36 (2008), pp. 1619--1648.
We use the fitted Pareto law to construct an accompanying approximation of the excess distribution function. A selection rule of the location of the excess distribution function is proposed based on a stagewise lack-of-fit testing procedure. Our main result is an oracle type inequality for the Kullback-Leibler loss of the obtained adaptive estimator.
Y. Chen, W. Härdle, V. Spokoiny, Portfolio value at risk based on independent components analysis, Journal of Computational and Applied Mathematics, 205 (2007), pp. 594--607.
Risk management technology applied to high-dimensional portfolios needs simple and fast methods for calculation of value at risk (VaR). The multivariate normal framework provides a simple off-the-shelf methodology but lacks the heavy-tailed distributional properties that are observed in data. A principle component-based method (tied closely to the elliptical structure of the distribution) is therefore expected to be unsatisfactory. Here, we propose and analyze a technology that is based on independent component analysis (ICA). We study the proposed ICVaR methodology in an extensive simulation study and apply it to a high-dimensional portfolio situation. Our analysis yields very accurate VaRs.
D. Belomestny, V. Spokoiny, Spatial aggregation of local likelihood estimates with applications to classification, The Annals of Statistics, 35 (2007), pp. 2287--2311.
This paper presents a new method for spatially adaptive local (constant) likelihood estimation which applies to a broad class of nonparametric models, including the Gaussian, Poisson and binary response models. The main idea of the method is given a sequence of local likelihood estimates (”weak” estimates), to construct a new aggregated estimate whose pointwise risk is of order of the smallest risk among all “weak” estimates. We also propose a new approach towards selecting the parameters of the procedure by providing the prescribed behavior of the resulting estimate in the simple parametric situation. We establish a number of important theoretical results concerning the optimality of the aggregated estimate. In particular, our “oracle” results claims that its risk is up to some logarithmic multiplier equal to the smallest risk for the given family of estimates. The performance of the procedure is illustrated by application to the classification problem. A numerical study demonstrates its nice performance in simulated and real life examples.
A. Samarov, V. Spokoiny, C. Vial, Component identification and estimation in nonlinear high-dimensional regression models by structural adaptation, Journal of the American Statistical Association, 100 (2005), pp. 429--445.
M. Giurcanu, V. Spokoiny, Confidence estimation of the covariance function of stationary and locally stationary processes, Statistics & Decisions. International Journal for Statistical Theory and Related Fields, 22 (2004), pp. 283--300.
V. Spokoiny, D. Mercurio, Statistical inference for time-inhomogeneous volatility models, The Annals of Statistics, 32 (2004), pp. 577--602.
W. Härdle, H. Herwatz, V. Spokoiny, Time inhomogeneous multiple volatility modelling, Journal of Financial Econometrics, 1 (2003), pp. 55-95.
D. Belomestny, V. Jentsch, M. Schreckenberg, Completion and continuation of nonlinear traffic time series: A probabilistic approach, Journal of Physics. A. Mathematical and General, 36 (2003), pp. 11369-11383.
When dealing with nonlinear time series of car traffic on highways, one of the outstanding problems to be solved is completion and continuation of data in space and time. To this end, the underlying process is decomposed into stochastic and deterministic components. The former is approximated by Gaussian white noise, while the latter refers, apart from always existing trends, to the space- and time-dependent jam propagation process. Jams are modelled in terms of dynamical Bayesian networks with radial basis functions involved. The models developed are used to tackle travel time estimation and prediction. Results are obtained for one of the most crowded traffic areas of Europe, namely the ring-like highway around Cologne.
L. Dümbgen, V. Spokoiny, Multiscale testing of qualitative hypotheses, The Annals of Statistics, 29 (2001), pp. 124--152.
W. Härdle, S. Sperlich, V. Spokoiny, Structural tests for additive regression, Journal of the American Statistical Association, 96 (2001), pp. 1333--1347.
J.L. Horowitz, V. Spokoiny, An adaptive, rate-optimal test of a parametric mean-regression model against a nonparametric alternative, Econometrica. Journal of the Econometric Society, 69 (2001), pp. 599--631.
M. Hristache, A. Juditsky, V. Spokoiny, Direct estimation of the index coefficient in a single-index model, The Annals of Statistics, 29 (2001), pp. 595--623.
S. Jaschke, U. Küchler, Coherent risk measures and good-deal bounds, Statistics & Probability Letters, 5 (2001), pp. 181--200.
V. Spokoiny, Data driven testing the fit of linear models, Mathematical Methods of Statistics, 10 (2001), pp. 465--497.
R. Liptser, V. Spokoiny, Deviation probability bound for martingales with applications to statistical estimation, Statistics & Probability Letters, 46 (2000), pp. 347--357.
R. Liptser, V. Spokoiny, On estimating a dynamic function of a stochastic system with averaging, Statistical Inference for Stochastic Processes. An International Journal Devoted to Time Series Analysis and the Statistics of Continuous Time Processes and Dynamical Systems, 3 (2000), pp. 225--249.
V. Spokoiny, Adaptive drift estimation for nonparametric diffusion model, The Annals of Statistics, 28 (2000), pp. 815--836.
H.-J. Mucha, H.-G. Bartel, Distance and data transformation, in: The SAS Encyclopedia of Archaeological Sciences, S.L. López Varela, ed., JohnWiley & Sons, Inc., 2018, pp. published online on 26.11.2018, DOI 10.1002/9781119188230.saseas0194 .
H.-J. Mucha, T.M. Gluhak, Finding groups in compositional data -- Some experiments, in: Big data clustering: Data preprocessing, variable selection, and dimension reduction, H.-J. Mucha, ed., WIAS Reports, 2017, pp. 97--105.
The talk is concerned with finding groups (clusters) in compositional data, that is nonnegative data with row sums (or column sums, respectively) equal to a constant, usually 1 in case of proportions or 100 in case of percentages. Without loss of generality, the cluster analysis of observations (row points) of compositional data is considered here, where the row profiles contains parts of some whole. Special distance functions between the profiles are proposed. Finally, applications to archaeometry are presented.
H.-J. Mucha, H.-G. Bartel, J. Dolata, Dual scaling classification and its application to archaeometry, in: Data Analysis, Machine Learning and Knowledge Discovery, M. Spiliopoulou, L. Schmidt-Thieme, R. Janning, eds., 48 of Studies in Classification, Data Analysis, and Knowledge Organization, Springer, Heidelberg et al., 2014, pp. 105--113.
H.-J. Mucha, H.-G. Bartel, Soft bootstrapping in cluster analysis and its comparison with other resampling methods, in: Data Analysis, Machine Learning and Knowledge Discovery, M. Spiliopoulou, L. Schmidt-Thieme, R. Janning, eds., 48 of Studies in Classification, Data Analysis, and Knowledge Organization, Springer, Heidelberg et al., 2014, pp. 97--104.
The bootstrap approach is resampling taken with replacement from the original data. Here we consider sampling from the empirical distribution of a given data set in order to investigate the stability of results of cluster analysis. Concretely, the original bootstrap technique can be formulated by choosing the following weights of observations: m i = n, if the corresponding object i is drawn n times, and m i = 0, otherwise. We call the weights of observations masses. In this paper, we present another bootstrap method, called soft bootstrapping, that consists of random change of the ?bootstrap masses? to some degree. Soft bootstrapping can be applied to any cluster analysis method that makes (directly or indirectly) use of weights of observations. This resampling scheme is especially appropriate for small sample sizes because no object is totally excluded from the soft bootstrap sample. At the end we compare different resampling techniques with respect to cluster analysis.
B. Maldonado, C. Morales-Merino, H.-J. Mucha, Ancient technology, modern science: Archaeometallurgy in northern Chile, in: Archäometrie und Denkmalpflege 2013, A. Hauptmann, O. Mecking, M. Prange, eds., Metalla, Sonderheft 6, Deutsches Bergbau-Museum Bochum, 2013, pp. 70--74.
H.-J. Mucha, H.-G. Bartel, C. Morales-Merino, Visualisation of cluster analysis results, in: Classification and Data Mining, A. Giusti, G. Ritter, M. Vichi, eds., Studies in Classification, Data Analysis, and Knowledge Organization, Springer, Berlin, 2013, pp. 261--270.
TH. Dickhaus, J. Gierl, Simultaneous test procedures in terms of p-value copulae, in: Proceedings of the 2nd Annual International Conference on Computational Mathematics, Computational Geometry & Statistics (CMCGS 2013), K. Chen, C. Raju, eds., Global Science and Technology Forum, Singapore, 2013, pp. 75--80.
H.-J. Mucha, J. Dolata, H.-G. Bartel, Classification of Roman tiles with stamp PARDALIVS, in: Algorithms from & for Nature and Life, B. Lausen, D. VAN DEN Poel, A. Ultsch, eds., Studies in Classification, Data Analysis, and Knowledge Organization, Springer, Heidelberg, 2013, pp. 309--317.
H.-J. Mucha, R. Naumann, G. Schneider, H.-G. Bartel, M. Daszkiewicz, Diskriminanzanalytische Überprüfung einer Klassifikation von Keramikfunden aus Uruk, in: Archäometrie und Denkmalpflege 2013, A. Hauptmann, O. Mecking, M. Prange, eds., Metalla, Sonderheft 6, Deutsches Bergbau-Museum Bochum, 2013, pp. 159--163.
S. Behrendt, H.-J. Mucha, H.-G. Bartel, D.P. Mielke, Zur Problematik der multivariaten statistischen Analyse umfangreicher P-RFA-Datenmengen phönizischer Keramik, in: Archäometrie und Denkmalpflege 2012, F. Schlütter, S. Greif, M. Prange, eds., Metalla, Sonderheft 5, Deutsches Bergbau-Museum Bochum, 2012, pp. 157--159.
J. Dolata, H.-J. Mucha, H.-G. Bartel, Eine neue Referenzgruppe für die römische Ziegelproduktion der I. Thrakerkohorte am Mittelrhein, in: Archäometrie und Denkmalpflege 2012, F. Schlütter, S. Greif, M. Prange, eds., Metalla, Sonderheft 5, Deutsches Bergbau-Museum Bochum, 2012, pp. 171--173.
C. Morales-Merino, H.-J. Mucha, H.-G. Bartel, Multivariate statistical analysis of clay and ceramic-data for provenance of Bronze Age pottery from Troia, in: Archäometrie und Denkmalpflege 2012, F. Schlütter, S. Greif, M. Prange, eds., Metalla, Sonderheft 5, Deutsches Bergbau-Museum Bochum, 2012, pp. 174--176.
H.-J. Mucha, H.-G. Bartel, An accelerated K-means algorithm based on adaptive distances, in: Challenges at the Interface of Data Analysis, Computer Science, and Optimization --- Proceedings of the 34th Annual Conference of the Gesellschaft für Klassifikation e. V., Karlsruhe, July 21--23, 2010, W. Gaul, A. Geyer-Schulz, L. Schmidt-Thieme, J. Kunze, eds., Studies in Classification, Data Analysis, and Knowledge Organization, Springer, Heidelberg, 2012, pp. 37--47.
H.-J. Mucha, Method selection in cluster analysis followed by built-in validation, in: Data Analysis Methods and its Applications, J. Pociecha, R. Decker, eds., Wydawnictwo C. H. Beck, Warszawa, 2012, pp. 125--142.
H.-G. Bartel, H.-J. Mucha, J. Dolata, Geochemical and statistical investigation of Roman stamped tiles of the Legio XXI Rapax, in: Classification as a Tool for Research. 11th IFCS Biennial Conference and 33rd Annual Conference of the Gesellschaft für Klassifikation e. V., Dresden, March 13--18, 2009, H. Locarek-Junge, C. Weihs, eds., Studies in Classification, Data Analysis, and Knowledge Organization, Springer, Berlin/Heidelberg, 2010, pp. 427--434.
J. Dolata, D. Lück, H.-G. Bartel, H.-J. Mucha, C. Wenzel, Analysen römischer Ziegel aus dem Kastellbad von Groß-Gerau: ihre statistische Auswertung und archäologische Bewertung, in: Archäometrie und Denkmalpflege 2010, O. Hahn, A. Hauptmann, D. Modarressi-Tehrani, M. Prange, eds., Metalla, Sonderheft 3, Deutsches Bergbau-Museum Bochum, 2010, pp. 129--131.
J. Dolata, H.-J. Mucha, H.-G. Bartel, Mathematisch-archäometrische Auswertung der GIS-gestützten Fundkartierung römischer Ziegel aus Mainz, in: Archäometrie und Denkmalpflege 2010, O. Hahn, A. Hauptmann, D. Modarressi--Tehrani, M. Prange, eds., Metalla, Sonderheft 3, Deutsches Bergbau-Museum Bochum, 2010, pp. 119--121.
J. Dolata, H.-J. Mucha, H.-G. Bartel, Mapping findspots of Roman military brickstamps in em Mogontiacum (Mainz) and archaeometrical analysis, in: Advances in Data Analysis, Data Handling and Business Intelligence, A. Fink, B. Lausen, W. Seidel, A. Ultsch, eds., Studies in Classification, Data Analysis, and Knowledge Organization, Springer, Heidelberg, 2010, pp. 595--603.
E. Haimerl, H.-J. Mucha, Comparing the stability of clustering results of dialect data based on several distance matrices, in: Classification as a Tool for Research. Proceedings of the 11th IFCS Biennial Conference and 33rd Annual Conference of the Gesellschaft für Klassifikation e. V., Dresden, March 13--18, 2009, H. Locarek-Junge, C. Weihs, eds., Studies in Classification, Data Analysis, and Knowledge Organization, Springer, Berlin/Heidelberg, 2010, pp. 665--672.
C. Morales-Merino, H.-J. Mucha, H.-G. Bartel, E. Pernicka, Clay sediments analysis in the Troad and its segmentation, in: Archäometrie und Denkmalpflege 2010, O. Hahn, A. Hauptmann, D. Modarressi--Tehrani, M. Prange, eds., Metalla, Sonderheft 3, Deutsches Bergbau-Museum Bochum, 2010, pp. 122--124.
P. Čížek, V. Spokoiny, Varying coefficient GARCH models, in: Handbook of Financial Time Series, T.G. Andersen, R.A. Davis, J.--P. Kreiss, T. Mikosch, eds., Springer, Berlin, 2009, pp. 169--185.
J. Dolata, H.-G. Bartel, H.-J. Mucha, Geochemische und statistische Erkundung der Herstellungsorte von Ziegeln der em Legio XXI Rapax, in: Oedenburg Fouilles Françaises, Allemandes et Suisses à Biesheim et Kunheim, Haut-Rhin, France. Volume 1: Les Camps Militaires Julio-Claudiens, M. Reddé, ed., 79 of Monographien des Römisch-Germanischen Zentralmuseums, Verlag des Römisch-Germanischen Zentralmuseums, Mainz, 2009, pp. 355-364.
C. Scott, G. Blanchard, Novelty detection: Unlabeled data definitely help, in: Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, April 16--18, 2009, Clearwater Beach, Florida, USA, D. VAN Dyke, M. Welling, eds., 5 of JMLR Workshop and Conference Proceedings, JMLR, 2009, pp. 464--471.
H.-J. Mucha, H.-G. Bartel, J. Dolata, Finding Roman brickyards in Germania Superior by model-based cluster analysis of archaeometric data, in: Proceedings of the 35th International Conference on Computer Applications and Quantitative Methods in Archaeology (CAA 2007), A. Posluschny, K. Lambers, I. Herzog, eds., 10 of Kolloquien zur Vor- und Frühgeschichte, Habelt Verlag, Bonn, 2009, pp. 360--365.
H.-J. Mucha, H.-G. Bartel, J. Dolata, Zur Klassifikation römischer Ziegel von Fundorten im südlichen Obergermanien. I. Multivariate statistische Analyse der archäometrischen Daten, in: Archäometrie und Denkmalpflege 2009, Jahrestagung in der Pinakothek der Moderne, München, 25.--28. März 2009, A. Hauptmann, H. Stege, eds., Metalla, Sonderheft 2, Deutsches Bergbau-Museum Bochum, 2009, pp. 144--146.
M. Elagin, V. Spokoiny, Locally time homogeneous time series modelling, in: Applied Quantitative Finance (2nd edition), W. Härdle, N. Hautsch, L. Overbeck, eds., Springer, Berlin, 2008, pp. 345--362.
H.-J. Mucha, Big data clustering: Data preprocessing, variable selection, and dimension reduction, Report no. 29, WIAS, Berlin, 2017, DOI 10.20347/WIAS.REPORT.29 .
L. Antoine, P. Pigato, Maximum likelihood drift estimation for a threshold diffusion, Preprint no. 2497, WIAS, Berlin, 2018, DOI 10.20347/WIAS.PREPRINT.2497 .
We study the maximum likelihood estimator of the drift parameters of a stochastic differential equation, with both drift and diffusion coefficients constant on the positive and negative axis, yet discontinuous at zero. This threshold diffusion is called the drifted Oscillating Brownian motion. The asymptotic behaviors of the positive and negative occupation times rule the ones of the estimators. Differently from most known results in the literature, we do not restrict ourselves to the ergodic framework: indeed, depending on the signs of the drift, the process may be ergodic, transient or null recurrent. For each regime, we establish whether or not the estimators are consistent; if they are, we prove the convergence in long time of the properly rescaled difference of the estimators towards a normal or mixed normal distribution. These theoretical results are backed by numerical simulations.
A. Lejay, P. Pigato, A threshold model for local volatility: Evidence of leverage and mean reversion effects on historical data, Preprint no. 2467, WIAS, Berlin, 2017, DOI 10.20347/WIAS.PREPRINT.2467 .
In financial markets, low prices are generally associated with high volatilities and vice-versa, this well known stylized fact usually being referred to as leverage effect. We propose a local volatility model, given by a stochastic differential equation with piecewise constant coefficients, which accounts of leverage and mean-reversion effects in the dynamics of the prices. This model exhibits a regime switch in the dynamics accordingly to a certain threshold. It can be seen as a continuous time version of the Self-Exciting Threshold Autoregressive (SETAR) model. We propose an estimation procedure for the volatility and drift coefficients as well as for the threshold level. Tests are performed on the daily prices of 21 assets. They show empirical evidence for leverage and mean-reversion effects, consistent with the results in the literature.
V. Avanesov, N. Buzun, Change-point detection in high-dimensional covariance structure, Preprint no. 2404, WIAS, Berlin, 2017.
In this paper we introduce a novel approach for an important problem of break detection. Specifically, we are interested in detection of an abrupt change in the covariance structure of a high-dimensional random process ? a problem, which has applications in many areas e.g., neuroimaging and finance. The developed approach is essentially a testing procedure involving a choice of a critical level. To that end a non-standard bootstrap scheme is proposed and theoretically justified under mild assumptions. Theoretical study features a result providing guaranties for break detection. All the theoretical results are established in a high-dimensional setting (dimensionality p n). Multiscale nature of the approach allows for a trade-off between sensitivity of break detection and localization. The approach can be naturally employed in an on-line setting. Simulation study demonstrates that the approach matches the nominal level of false alarm probability and exhibits high power, outperforming a recent approach.
A. Suvorikova, Statistical inference with optimal transport, Spring School ``Structural Inference 2018'' and Closing Workshop, March 4 - 9, 2018.
A. Suvorikova, Statistical inference with optimal transport, WIAS-Day, February 22 - 23, 2018.
P. Pigato, Estimation of piecewise-constant coefficients in a stochastic differential equation, The 40th Conference on Stochastic Processes and their Applications - SPA 2018, University of Gothenburg, Göteborg, Sweden, June 13, 2018.
P. Dvurechensky, Statistical optimal transport, WIAS-Day, February 23, 2018.
V. Spokoiny, Adaptive nonparametric clustering, Multiscale Problems in Materials Science and Biology: Analysis and Computation, January 8 - 12, 2018, Tsinghua University, Yau Mathematical Sciences Center, Sanya, Hainan, China, January 10, 2018.
V. Spokoiny, Bootstrap confidence sets for spectral projectors of sample covariance, 12th International Vilnius Conference on Probability Theory and Mathematical Statistics and 2018 IMS Annual Meeting on Probability and Statistics, July 2 - 6, 2018, Vilnius University, Lithuanian Mathematical Society and the Institute of Mathematical Statistics, Lithuania, July 5, 2018.
V. Spokoiny, Gaussian process forecast with multidimensional distributional input, 4th Conference of the International Society for Nonparametric Statistics, June 11 - 15, 2018, University of Salerno, Italy, June 15, 2018.
V. Spokoiny, Inference for spectral projectors, Statistical Inference for Structured High-dimensional Models, March 11 - 16, 2018, Mathematisches Forschungsinstitut Oberwolfach, March 14, 2018.
V. Spokoiny, Large ball probability with applications in statistics, Mathematical Workshop of the School of Applied Mathematics and Computer Science, Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation, November 30, 2018.
V. Spokoiny, Large ball probability with applications to statistical inference, 6th Princeton Day of Statistics, November 9, 2018, Princeton University, Department of Operations Research and Financial Engineering, USA, November 9, 2018.
V. Spokoiny, Prior impact in Bayesian inference, Internatioal Seminar & Workshop:``Stochastic dynamics on large networks: Prediction and inference", October 15 - 16, 2018, Max-Planck-Institut für Physik komplexer Systeme, Dresden, October 15, 2018.
V. Spokoiny, Structured nonparametric and high-dimensional statistics, 2018 Joint Meeting of the Korean Mathematical Society and the German Mathematical Society, October 3 - 6, 2018, Korean Mathematical Society, Seoul, Korea (Republic of), October 5, 2018.
A. Suvorikova, Construction of confidence sets in 2-Wasserstein space, Haindorf Seminar 2017, January 24 - 28, 2017, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, Hejnice, Czech Republic, January 26, 2017.
A. Suvorikova, Construction of confidence sets in 2-Wasserstein space, Machine Learning Seminar, Université Paul-Sabatier, Institut de Mathématiques de Toulouse, France, December 1, 2017.
A. Suvorikova, Construction of non-asymptotic confidence sets in 2-Wasserstein space, Spring School ``Structural Inference'' 2017, Bad Malente, March 5 - 10, 2017.
P. Pigato, Estimation of the parameters of a diffusion with discontinuous coefficients, 7th Annual ERC Berlin-Oxford Young Researchers Meeting on Applied Stochastic Analysis, May 18 - 20, 2017, WIAS-Berlin, May 20, 2017.
H.-J. Mucha, About the influence of multiple points of bootstrapping on the validation of clustering results, European Classification and Data Analysis Conference 2017 (ECDA2017), September 27 - 29, 2017, Wrocław University of Economics, Poland, September 27, 2017.
H.-J. Mucha, Big data clustering: Comparison of the performance of a new fast pre-clustering and subsampling, German Polish Seminar on Data Analysis and Applications 2017, September 25 - 26, 2017, Wrocław University of Economics, Poland, September 26, 2017.
H.-J. Mucha, Hierarchical clustering of big data using a special bootstrapping version, AG DANK Herbsttagung, November 17 - 18, 2017, Gesellschaft für KlassifikationGesellschaft für Klassifikation, Data Science Society, November 18, 2017.
A. Suvorikova, Bootstrap confidence sets for Wasserstein barycenters, Meeting in Mathematical Statistics 2016 ``Advances in Nonparametric and High-dimensional Statistics'', December 12 - 16, 2016, Fréjus, France, December 16, 2016.
A. Suvorikova, Bootstrap procedure in the space of Gaussian measures, Information Technology and Systems 2016, September 25 - 30, 2016, Russian Academy of Sciences, Institute for Information Transmission Problems, St. Petersburg.
A. Suvorikova, Multiscale change point detection, Georg-August-Universität Göttingen, Institut für Mathematische Stochastik, November 9, 2016.
CH. Bayer, The forward-reverse method for conditional Markov processes, Bayes in Paris, École Nationale de la Statistique et de l'Administration Économique, Paris, France, January 28, 2016.
V. Spokoiny, Adaptive weights clustering, Mathematisches Kolloquium, Universität Ulm, Institut für Analysis, November 18, 2016.
V. Spokoiny, Clustering using adaptive weights, Yandex, Moscow, Russian Federation, October 28, 2016.
V. Spokoiny, Inference for Structural Nonparametrics, Open Access: urlhttp://www.mathnet.ru/php/conference.phtml?option_lang=eng&eventID=25&confid=872, February 15 - March 1, 2016, Independent University of Moscow (IUM), Russian Federation.
V. Spokoiny, Inference for structured regression, Meeting in Mathematical Statistics 2016 ``Advances in Nonparametric and High-dimensional Statistics'', December 12 - 16, 2016, Fréjus, France, December 15, 2016.
N. Buzun, Multiplier bootstrap with misspecified models, Statistics Mathematics and Applications, August 31 - September 4, 2015, La Villa Clythia, Fréjus, France, September 1, 2015.
K. Schildknecht, Simultaneous statistical inference for epigenetic data, 61. Biometrischen Kolloquium in Dortmund, March 16 - 18, 2015, Technische Universität Dortmund, Fakultät Statistik, March 18, 2015.
J. Stange, On the correlation structure of test statistics in genetic case-control association studies, 61. Biometrischen Kolloquium in Dortmund, March 16 - 18, 2015, Technische Universität Dortmund, Fakultät Statistik, March 17, 2015.
CH. Bayer, The forward-reverse method for conditional Markov processes, MInisymposium ``Multilevel Monte Carlo Methods and Applications'' of the 8th International Congress on Industrial and Applied Mathematics (ICIAM2015), August 10 - 14, 2015, International Council for Industrial and Applied Mathematics, Beijing, China, August 10, 2015.
CH. Bayer, The forward-reverse method for conditional diffusion processes, Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015), January 6 - 9, 2015, King Abdullah University of Science and Technology (KAUST), Jeddah, Saudi Arabia, January 6, 2015.
CH. Bayer, The forward-reverse method for conditional diffusion processes, Oberseminar Finanz- und Versicherungsmathematik, Technische Universität München, Fakultät für Mathematik, January 19, 2015.
CH. Bayer , The forward-reverse method for conditional Markov processes, International Conference on Scientific Computation and Differential Equations, Universität Potsdam, Institut für Mathematik, September 16, 2015.
V. Spokoiny, Bootstrap tuning in model selection, Lomonosov State University, Faculty of Computer Sciences, Moscow, Russian Federation, September 18, 2015.
V. Spokoiny, Predictive modeling: Methods and applications, Skolkov Institute of Science and Technology, Moscow, Russian Federation, August 27, 2015.
M. Zhilova, Non-asymptotic confidence bounds via multiplier bootstrap, CRC 649 "Economic Risk" Conference, July 10 - 12, 2014, Humboldt-Universität zu Berlin, Sonderforschungsbereich 649: Ökonomisches Risiko, July 11, 2014.
M. Zhilova, Parametric confidence sets using multiplier bootstrap, Premoday, February 21, 2014, Russian Academy of Sciences, Institute of Information Transmission Problems (Kharkevich Institute), Moscow, Russian Federation, February 21, 2014.
M. Zhilova, Uniform confidence bands for generalized regression via multiplier bootstrap, International Symposium on Financial Engineering and Risk Management 2014, June 27 - 28, 2014, Central University of Finance and Economics, Beijing, China, June 27, 2014.
M. Zhilova, Uniform confidence bands in generalized regression via multiplier bootstrap, The 3rd Institute of Mathematical Statistics Asia Pacific Rim Meeting, June 29 - July 3, 2014, Taipei, Taiwan, Province Of China, July 1, 2014.
M. Zhilova, Uniform confidence sets via multiplier bootstrap, New Horizons in Statistical Decision Theory, September 7 - 13, 2014, Mathematisches Forschungsinstitut Oberwolfach, September 9, 2014.
A. Andresen, Finite sample analysis of M-estimators, International Workshop ``Advances in Optimization and Statistics'', May 15 - 16, 2014, Russian Academy of Sciences, Institute of Information Transmission Problems (Kharkevich Institute), Moscow, May 16, 2014.
A. Andresen, Finite sample analysis of maximum likelihood estimators, Kolloquium über Mathematische Statistik und Stochastische Prozesse, Universität Hamburg, April 22, 2014.
R. Hildebrand, Spectrahedral cones generated by rank 1 matrices, Workshop ``Real Algebraic Geometry With A View Toward Systems Control and Free Positivity'', April 6 - 12, 2014, Mathematisches Forschungsinstitut Oberwolfach, April 11, 2014.
K. Schildknecht, A two-stage hierarchical multiple test procedure based on the asymptotically optimal rejection curve, 60. Biometrisches Kolloquium, Bremen, March 10 - 13, 2014.
K. Schildknecht, A two-stage hierarchical multiple test procedure based on the asymptotically optimal rejection curve, Workshop Adaptive Designs and Multiple Testing Procedures, Internationalen Biometrischen Gesellschaft, Basel, Switzerland, June 5, 2014.
J. Stange, Computation of an effective number of simultaneous $chi^2$ tests, International Workshop ``Advances in Optimization and Statistics'', May 15 - 16, 2014, Russian Academy of Sciences, Institute of Information Transmission Problems (Kharkevich Institute), Moscow, May 15, 2014.
J. Stange, Multiple testing adjustments with copula models, 27th International Biometric Conference (IBC 2014), Florence, Italy, July 6 - 11, 2014.
J. Stange , Multiple testing adjustments with copula models, Haindorf Seminar 2014, February 6 - 8, 2014, Humboldt-Universitát zu Berlin, Wirtschaftswissenschaftliche Fakultät, Hejnice, Czech Republic, February 6, 2014.
N. Willrich, Model selection by Lepski's method for penalized likelihood, International Workshop ``Advances in Optimization and Statistics'', May 15 - 16, 2014, Russian Academy of Sciences, Institute of Information Transmission Problems (Kharkevich Institute), Moscow, May 16, 2014.
TH. Dickhaus, T. Bodnar, False discovery rate control under Archimedean copula, 27th International Biometric Conference (IBC 2014), July 6 - 11, 2014, International Biometric Society, Florence, Italy, July 7, 2014.
TH. Dickhaus, Randomized p-values for multiple testing of composite null hypotheses, 60. Biometrisches Kolloquium, March 11 - 13, 2014, Universität Bremen, March 12, 2014.
TH. Dickhaus, Simultaneous Bayesian analysis of contingency tables in genetic association studies, Bayesian Biostatistics 2014, July 2 - 5, 2014, University of Zurich, Switzerland, July 2, 2014.
TH. Dickhaus, Simultaneous Bayesian analysis of contingency tables in genetic association studies, International Workshop ``Advances in Optimization and Statistics'', May 15 - 16, 2014, Russian Academy of Sciences, Institute of Information Transmission Problems (Kharkevich Institute), Moscow, May 15, 2014.
P. Friz, Implied, local and stochastic volatility: Analytical results in Heston and Stein Stein, Thematic Cycle on Robust Management in Finance, Workshop ``Model Approximation and Numerical Methods'' (February -- March 2014), Institut Louis Bachelier, Paris, France, March 14, 2014.
H.-J. Mucha, Bottom-up variable selection in cluster analysis using bootstrapping: A proposal, European Conference on Data Analysis (ECDA 2014), July 2 - 4, 2014, Jacobs University Bremen gGmbH, July 4, 2014.
H.-J. Mucha, From univariate to multivariate clustering with application to P-ED-XRF data, Workshop ``Application of Portable Energy-dispersive X-ray Fluorescence to the Analysis of Archaeological Ceramics'', June 20 - 21, 2014, Excellence Cluster TOPOI, Berlin, June 21, 2014.
H.-J. Mucha, Variable selection in cluster analysis by means of bootstrapping, International Workshop ``Advances in Optimization and Statistics'', May 15 - 16, 2014, Russian Academy of Sciences, Institute of Information Transmission Problems (Kharkevich Institute), Moscow, May 16, 2014.
V. Spokoiny, Bernstein--von Mises theorem for a quasi-posterior, International Symposium on Financial Engineering and Risk Management 2014, June 27 - 28, 2014, Central University of Finance and Economics, Beijing, China, June 27, 2014.
V. Spokoiny, Bootstrap confidence sets under a model misspecification, Workshop ``New Horizons in Statistical Decision Theory'', September 7 - 13, 2014, Mathematisches Forschungsinstitut Oberwolfach, September 8, 2014.
V. Spokoiny, Construction of the sharp confidence bands using multiplier bootstrap, Advances in Stochastic Analysis, September 3 - 5, 2014, National Research University -- Higher School of Economics, Laboratory of Stochastic Analysis and its Applications, Moscow, Russian Federation, September 5, 2014.
V. Spokoiny, Modern Parametric Statistics, February 10 - 25, 2014, Independent University of Moscow, Russian Federation.
S. Becker, The propagation-separation approach: Choice of the adaptation bandwidth and its (in-)dependence of the unknown parameter, PreMoLab Workshop on: Advances in predictive modeling and optimization, May 16 - 17, 2013, WIAS-Berlin, May 17, 2013.
M. Zhilova, Resampling based uniform confidence bands in quantile regression, Tagung des SFB 649 "Ökonomisches Risiko" in Motzen, June 20 - 22, 2013, Humboldt-Universität zu Berlin, School of Business and Economics, June 22, 2013.
M. Zhilova, Uniform confidence bands in local estimation, 29th European Meeting of Statisticians (EMS), July 20 - 25, 2013, Eötvös Loránd University, Budapest, Hungary, July 20, 2013.
M. Zhilova, Uniform confidence bands in local estimation, Premoday February 2013, Moscow Institute of Physics and Technology State University, Russian Federation, February 22, 2013.
M. Zhilova, Uniform confidence bands in local parametric estimation, Haindorf-Seminar 2013, February 7 - 9, 2013, Humboldt-Universität zu Berlin, Czech Republic, February 9, 2013.
R. Hildebrand, Convex projective programming, International Conference on Continuous Optimization (ICCOPT2013), July 29 - August 1, 2013, Universidade Nove de Lisboa, Portugal, July 31, 2013.
R. Hildebrand, Scaling relationships between the copositive cone and the cone of sum of squares polynomials, International Conference on Continuous Optimization (ICCOPT2013), July 29 - August 1, 2013, Universidade Nove de Lisboa, Portugal, July 30, 2013.
TH. Dickhaus, Bootstrap methods for simultaneous statistical inference in dynamic factor models, Princeton-Humboldt Conference 2013, November 1 - 2, 2013, Princeton University, Department of Operations Research and Financial Engineering, USA, November 1, 2013.
H.-J. Mucha, Assessment of stability in partitional clustering using resampling techniques, Third Bilateral German-Polish Symposium on Data Analysis and Its Applications (GPSDAA 2013), September 26 - 28, 2013, Technische Universität Dresden, September 27, 2013.
H.-J. Mucha, Resampling techniques in cluster analysis: Is subsampling better than bootstrapping?, European Conference on Data Analysis, July 10 - 12, 2013, University of Luxembourg, Luxembourg, July 11, 2013.
H.-J. Mucha, Selection of variables in cluster analysis using resampling techniques: A proposal, Conference of the International Federation of Classification Societies (IFCS), July 14 - 17, 2013, Tilburg University, Netherlands, July 16, 2013.
H.-J. Mucha, Variables election in cluster analysis using resampling techniques, British Classification Society Meeting 2013, November 8 - 9, 2013, University College London, Department of Statistical Science, UK, November 8, 2013.
V. Spokoiny, Identification and critical dimension in semiparametric estimation, Workshop ``Mathematical Statistics of Partially Identified Objects'', April 21 - 27, 2013, Mathematisches Forschungsinstitut Oberwolfach, April 21, 2013.
V. Spokoiny, Robust clustering using adaptive weights, International Workshop on Statistical Learning, June 26 - 28, 2013, Moscow Institute of Physics and Technology, PreMoLab, Russian Federation, June 27, 2013.
M. Zhilova, Uniform properties of local maximum likelihood estimate, 55-th Scientific conference of Moscow Institute of Physics and Technology, November 19 - 24, 2012, Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation, November 24, 2012.
M. Becker, CP-detection, outlook, ECONS Projekttreffen, PIK Potsdam, December 12, 2012.
M. Becker, Change point detection via universal compressors, PreMoLab: Moscow-Berlin Stochastic and Predictive Modeling, May 30 - June 2, 2012, Russian Academy of Sciences, Institute for Information Transmission Problems (Kharkevich Institute), Moscow, May 31, 2012.
M. Becker, Changepoint detection via universal compressors, ECONS - Projekttreffen, WIAS - Berlin, June 7, 2012.
J. Dolata, H.-J. Mucha, H.-G. Bartel, Eine neue Referenzgruppe für die römische Ziegelproduktion der I. Thrakerkohorte am Mittelrhein, Archäometrie und Denkmalpflege 2012, Tübingen, March 27 - 31, 2012.
C. Morales-Merino, H.-J. Mucha, H.-G. Bartel, Multivariate statistical analysis of clay and ceramic-data for provenance of Bronze age pottery from Troia, Archäometrie und Denkmalpflege 2012, Tübingen, March 27 - 31, 2012.
M. Zhilova, Quasi MLE in Instrumental variable model, PreMoDay II, March 16, 2012, Russian Academy of Sciences, Institute for Information Transmission Problems (Kharkevich Institute), Russian Federation, March 16, 2012.
H.-J. Mucha, Clustering and visualisation based on dual scaling, 4th Japanese-German Symposium on Classification (JGSC2012), March 8 - 10, 2012, Doshisha University, Kyoto, Japan, March 9, 2012.
H.-J. Mucha, Dual scaling classification and its application to archaeometry, 36th Annual Conference of the German Classification Society, August 1 - 3, 2012, Universität Hildesheim, Information Systems and Machine Learning Lab (ISMLL), August 2, 2012.
H.-J. Mucha, Measures of incomparability and of inequality and their application, 10th Workshop on Partial Order, Theory and Application: Multi-Indicator Systems and Modelling in Partial Order, September 27 - 28, 2012, Hochschule für Technik und Wirtschaft Berlin, September 28, 2012.
H.-J. Mucha, Soft bootstrapping and its comparison with other resampling methods, 36th Annual Conference of the German Classification Society, August 1 - 3, 2012, Universität Hildesheim, Information Systems and Machine Learning Lab (ISMLL), August 2, 2012.
H.-J. Mucha, Visualisation and cluster analysis, Herbsttagung der AG Datenanalyse und numerische Klassifikation, October 5 - 6, 2012, Landschaftsverband Rheinland, LVR-Amt für Bodendenkmalpflege im Rheinland, Bonn, October 6, 2012.
M. Becker, Change point detection via universal compressors, ECONS (Evolving Complex Networks) Spring School and Workshop, March 28 - 31, 2011, Potsdam Institut für Klimafolgenforschung, Wandlitz, March 29, 2011.
S. Becker, Structural adaptive smoothing in R3xS2 with application to diffusion weighted imaging, Statistics Mathematics and Applications (StatMathAppli 2011), August 29 - September 2, 2011, Fréjus, France, September 1, 2011.
S. Becker, Structural adaptive smoothing in R3xS2 with application to diffusion weighted imaging, Chemnitz Symposium on Inverse Problems 2011, September 22 - 23, 2011, Technische Universität, Fakultät für Mathematik, September 22, 2011.
E. Diederichs, Modellselektion durch Semidefinite Relaxation, Jahrestagung der Deutschen Mathematiker-Vereinigung (DMV) 2011, September 19 - 22, 2011, Universität zu Köln, Mathematisches Institut, September 21, 2011.
E. Diederichs, Recent trends in large scale optimization, ECONS (Evolving Complex Networks) Spring School and Workshop, March 28 - 31, 2011, Potsdam Institut für Klimafolgenforschung, Wandlitz, March 31, 2011.
V. Panov, Non-Gaussian component analysis, ECONS Spring School and Workshop, March 28 - 31, 2011, Potsdam Institut für Klimaforschung, March 31, 2011.
S. Becker, Model-free structural adaptive smoothing of diffusion weighted images, Workshop on Statistics and Neuroimaging 2011, November 23 - 25, 2011, WIAS, November 24, 2011.
M. Becker, Current state of research on SILT and CP-detection, ECONS-projekt-meeting, November 21, 2011, Leibniz-Institut für Gewässerökologie und Binnenfischerei (IGB), November 21, 2011.
E. Diederichs, Sparse non-Gaussian component analysis, Structural Methods of Data Analysis and Optimization, December 15 - 17, 2011, Russian Academy of Sciences, Institute for Information Transmission Problems (Kharkevich Institute), Moscow, December 16, 2011.
H.-J. Mucha, Geochemical and statistical investigation of clay deposits in the troad and its implication for provenance of bronze age fine pottery from Troia, Joint Conference of the German Classification Society (GfKl), German Association for Pattern Recognition (DAGM), Symposium of the International Federation of Classification Societies (IFCS), August 30 - September 2, 2011, Goethe Universität Frankfurt am Main, September 1, 2011.
H.-J. Mucha, Method selection in cluster analysis followed by built-in validation, Second Bilateral German-Polish Symposium on Data Analysis and Its Applications (GPSDAA 2011), April 14 - 16, 2011, Cracow University of Economics, Poland, April 16, 2011.
V. Spokoiny, Alternating and semiparametric efficiency, Conference in Honour of Joel Horowitz, June 23 - 24, 2011, Centre for Microdata Methods and Practice, London, UK, June 24, 2011.
V. Spokoiny, Methods of dimension reduction, ECONS (Evolving Complex Networks) Spring School and Workshop, March 28 - 30, 2011, Potsdam Institut für Klimafolgenforschung, March 29, 2011.
V. Spokoiny, Non-Gaussian component analysis using semi-definite relaxation, MASCOT NUM (Méthodes d'Analyse Stochastique pour les COdes et Traitements NUMériques) 2011 Workshop, March 23 - 25, 2011, Villard de Lans, France, March 25, 2011.
V. Spokoiny, Parametric inference. Revisited, 5èmes Journées Statistiques du Sud, June 14 - 16, 2011, Université Nice Sophia Antipolis, Faculté des Sciences, June 15, 2011.
V. Spokoiny, Structure adaptive estimation by alternating, Workshop ``Stuctural Inference Day'', April 18, 2011, Universität Hamburg, April 18, 2011.
H.-J. Mucha, Classification based on dual scaling, Herbsttagung der AG DA-NK der GfKl, November 11 - 12, 2011, Heinrich Heine Universität Düsseldorf, November 11, 2011.
H.-J. Mucha, Classification of roman tiles with stamp Pardalis, Joint Conference of the German Classification Society (GfKl), German Association for Pattern Recognition (DAGM), Symposium of the International Federation of Classification Societies (IFCS), August 30 - September 2, 2011, Goethe Universität Frankfurt am Main, September 1, 2011.
V. Spokoiny, Alternating and semiparametric efficiency, Workshop ``Very High Dimensional Semiparametric Models'', October 3 - 7, 2011, Mathematisches Forschungsinstitut Oberwolfach, October 4, 2011.
A. Beinrucker, An application of Gaussian processes to Bayesian statistics, Seminar ``Gaussian Processe'', Universität Potsdam, Fachbereich Mathematik, November 25, 2010.
A. Beinrucker, Classification using FISTA, MASH Meeting, November 8 - 9, 2010, EU-Projekt MASH, Prag, Czech Republic, November 9, 2010.
A. Beinrucker, MASH --- Massive sets of heuristics, Machine Learning Summer School, Canberra, Australia, September 27 - October 6, 2010.
N. Krämer, The degrees of freedom of partial least squares regression, DAGStat 2010, March 23 - 26, 2010, Technische Universität Dortmund, Fakultät Statistik, March 23, 2010.
G. Blanchard, On optimal rates for kernel conjugate gradient regularization under random design and noise, Mini Special Semester on Inverse Problems, Part II: ``Inverse Problems in Data Driven Modelling'', July 19 - 22, 2010, Johann Radon Institute for Computational and Applied Mathematics (RICAM), Linz, Austria, July 20, 2010.
G. Blanchard, Optimal rate for kernel conjugate gradient regularization, Workshop "Modern Nonparametric Statistics: Going Beyond Asymptotic Minimax", March 29 - April 2, 2010, Mathematisches Forschungsinstitut Oberwolfach, March 30, 2010.
N. Krämer, Modeling of high-dimensional data with partial least squares, University of Leeds, Faculty of Medicine and Health, Leeds Institute of Genetics, Health and Therapeutics, UK, September 2, 2010.
N. Krämer, Regularized estimation of large-scale gene associaton networks, 55. GMDS-Jahrestagung: Effiziente und wirtschaftliche Gesundheitsversorgung von heute und morgen --- nur mit Medizinischer Dokumentation, Medizinischer Informatik, Medizinischer Biometrie und Epidemiologie, September 6 - 7, 2010, Hochschule Mannheim, Institut für Medizinische Informatik, September 6, 2010.
N. Krämer, The consistency of partial least spuares and conjugate gradient regularization, 73rd Annual Meeting of the Institute of Mathematical Statistics, August 9 - 13, 2010, Institute of Mathematical Statistics (IMS), Sweden, August 13, 2010.
P. Mathé, The Lepskii balancing principle for conjugate gradient regularization, Workshop on Inverse Problems: Developments in Theory and Applications (IP-TA 2010), February 8 - 12, 2010, Stefan Banach International Mathematical Center, Warsaw, Poland, February 9, 2010.
H.-J. Mucha, An accelerated K-means algorithm based on adaptive distances, 34th Annual Conference of the German Classification Society (GfKl) and International Symposium on the Data Analysis Interface, July 21 - 23, 2010, Karlsruhe Institute of Technology (KIT), July 22, 2010.
H.-J. Mucha, Automatische Klassifikation (Clusteranalyse), IBM SPSS Kundentag 2010, June 8, 2010, SPSS GmbH Software, München, June 8, 2010.
H.-J. Mucha, Pairwise data clustering accompanied by validation and visualisation, 3rd German-Japanese Workshop ``Advances in Data Analysis and Related New Techniques and Applications'', July 20 - 21, 2010, Karlsruhe Institute of Technology (KIT), July 20, 2010.
V. Spokoiny, Semidefinite optimization in instrumental variable estimation with shape constraints, Conference on ``Shape Restrictions in Non- and Semiparametric Estimation of Econometric Models'', November 1 - 7, 2010, Northwestern University, Department of Statisics, Chicago, USA, November 6, 2010.
V. Spokoiny, Sparse non-Gaussian component analysis, Journées STAR 2010, October 21 - 22, 2010, Université Rennes II, France, October 22, 2010.
V. Spokoiny, Sparse non-Gaussian component analysis, School of Mathematics Colloquium, November 19, 2010, University of Edinburgh, School of Mathematics, UK, November 19, 2010.
V. Spokoiny, Sparse non-Gaussian component analysis, University of Cambridge, Faculty of Mathematics, Statistical Laboratory, UK, May 7, 2010.
V. Spokoiny, Statistical estimation: Modern view, Rencontres de Statistiques Mathématiques 10, December 13 - 17, 2010, Centre International de Rencontres Mathématiques (CIRM), Luminy, France, December 16, 2010.
V. Spokoiny, Statistical estimation: Modern view, Rencontres de Statistiques Mathématiques 10, December 13 - 17, 2010, Centre International de Rencontres Mathématiques (CIRM), Luminy, France, December 15, 2010.
V. Panov, Solving classification task using detection of non-Gaussian components, Haindorf Seminar 2009, February 12 - 15, 2009, Humboldt-Universität zu Berlin, CASE --- Center for Applied Statistics and Economics, Hejnice, Czech Republic, February 14, 2009.
G. Blanchard, Non-asymptotic adaptive control of the family-wise error rate in multiple testing, Journées Statistiques du Sud 2009, June 17 - 19, 2009, Université de Provence Aix-Marseille I, Porquerolles, France, June 17, 2009.
H.-J. Mucha, Clustering accompanied by visualization and validation, 11th Conference of the International Federation of Classification Societies (IFCS 2009), March 13 - 18, 2009, Technische Universität Dresden, March 13, 2009.
H.-J. Mucha, Explorative statistische Methoden in der Archäometrie: von Daten zu Distanzen und weiter zu Gruppierungen, Curt-Engelhorn-Zentrum Archäometrie Mannheim, July 9, 2009.
H.-J. Mucha, Geochemical and statistical investigation of Roman stamped tiles of the Legio XXI Rapax, 11th Conference of the International Federation of Classification Societies (IFCS 2009), March 13 - 18, 2009, Technische Universität Dresden, March 14, 2009.
H.-J. Mucha, Remarks on Validation of Hierarchical Clustering, First Bilateral German-Polish Symposium on Data Analysis and its Applications, October 8 - 10, 2009, RWTH Aachen, October 10, 2009.
H.-J. Mucha, Zur Klassifikation römischer Ziegel von Fundorten im südlichen Obergermanien, Tagung ``Archäometrie und Denkmalpflege 2009'', March 25 - 28, 2009, Pinakothek der Moderne, München, March 26, 2009.
V. Spokoiny, Cross concentration in semiparametric estimation, Rencontres de Statistiques Mathématiques 9, December 14 - 18, 2009, International Center for Mathematical Meetings (CIRM), Marseille, France, December 15, 2009.
V. Spokoiny, Non-Gaussian component analysis, Workshop ``Semiparametric and Nonparametric Methods in Econometrics'', April 4 - 11, 2009, Banff International Research Station for Mathematical Innovation and Discovery, Canada, April 6, 2009.
V. Spokoiny, Saddle point model selection, 2nd Humboldt-Princeton Conference: Perceiving and Measuring Financial Risk: Credit, Energy and Illiquidity, October 30 - 31, 2009, Princeton University, USA, October 30, 2009.
M. Elagin, Comparison of some methods for the adaptive volatility estimation, Haindorf Seminar 2008 (Klausurtagung des SFB 649), February 7 - 10, 2008, Hejnice, Czech Republic, February 9, 2008.
E. Giacomini, Dynamic semiparametric factor modus in risk preferences estimation, 2008 North American Winter Meetings of the Econometric Society, January 4 - 6, 2008, North American Econometric Society, New Orleans, USA, January 6, 2008.
H.-J. Mucha, Clustering, multivariate visualization and validation, 30th Fall Meeting of the German Classification Society, Berlin, Gesellschaft für Klassifikation e.V., Berlin, November 14, 2008.
H.-J. Mucha, Mapping find spots of Roman military brickstamps in Mogontiacum (Mainz) and archeometrical analysis, Annual Meeting of Gesellschaft für Klassifikation, July 16 - 18, 2008, Helmut-Schmidt-Universität, Hamburg, August 17, 2008.
A. Rohde, Spatially adaptive comparison of multivariate samples via randomization, Université Paris VI ``Pierre et Marie Curie'', France, March 10, 2008.
A. Rohde, Spatially adaptive comparison of multivariate samples via randomization, Universität Bern, Switzerland, February 20, 2008.
N. Serdyukova, Dependence on the dimension for complexity of approximation of random fields, 4. Doktorandenkonferenz Stochastik, September 4 - 6, 2008, Technische Universität Berlin, September 6, 2008.
V. Spokoiny, Foundations and applications of modern nonparametric statistics (block lecture), May 15 - 16, 2008, Graduate School ``Mathematics and Practice'', Technische Universität Kaiserslautern/Fraunhofer Institut für Techno- und Wirtschaftsmathematik.
V. Spokoiny, Local parametric modeling of nonstationary time series, International Workshop on Recent Advances in Time Series Analysis, June 8 - 11, 2008, University of Cyprus, Department of Mathematics and Statistics, Protaras, June 11, 2008.
K. Tabelow, Structural adaptive smoothing in medical imagine, WIAS Day, WIAS Berlin, February 22, 2007.
Ladle stirring is an important step of the steelmaking process to homogenize the temperature and the chemical composition of the liquid steel and to remove inclusions before casting. Gas is injected from the bottom of the bath to induce a turbulent flow of the liquid steel. Multiphase modeling of ladle stirring can become computationally expensive, especially when used within optimal flow control problems. This paper focuses therefore on single-phase flow models. It aims at improving the existing models from the literature. Simulations in a 2d axial-symmetrical configuration, as well as, in a real 3d laboratory-scale ladle, are performed. The results obtained with the present model are in a relative good agreement with experimental data and suggest that it can be used as an efficient model in optimal flow control problems.
N. Buzun, V. Avanesov, Bootstrap for change point detection, Preprint no. arXiv:1710.07285, Cornell University Library, arXiv.org, 2017.
In this paper we introduce a novel approach for an important problem of change point detection. Specifically, we are interested in detection of an abrupt change in the covariance structure of a high-dimensional random process -- a problem, which has applications many areas e.g., neuroimaging and finance. The developed approach is essentially a testing procedure requiring a proper choice of a critical level. To that end a non-standard bootstrap scheme is proposed and theoretically justified under mild assumptions. Multiscale nature of the approach allows for a trade-off between sensitivity of change-point detection and localization of it. The approach can be naturally used in an on-line setting. A simulation study demonstrates that the approach matches the nominal level of false alarm probability and exhibits high power, outperforming competing approaches.
V. Spokoiny, W. Wang, W.K. Härdle, Local quantile regression, Preprint no. arXiv:1208.5384, Cornell University Library, arXiv.org, 2012.
J. Kampen, On the multivariate Burgers equation and the incompressible Navier--Stokes equation (electronic only), Preprint no. arXiv:0910.5672, Cornell University Library, arXiv.org, 2009.
S. Arlot, G. Blanchard, E. Roquain, Some non-asymptotic results on resampling in high dimension, I: Confidence regions, II: Multiple tests (electronic only), Preprint no. arXiv:0712.0775, Cornell University Library, arXiv.org, 2009.
We study generalized bootstrap confidence regions for the mean of a random vector whose coordinates have an unknown dependency structure. The random vector is supposed to be either Gaussian or to have a symmetric and bounded distribution. The dimensionality of the vector can possibly be much larger than the number of observations and we focus on a non-asymptotic control of the confidence level, following ideas inspired by recent results in learning theory. We consider two approaches, the first based on a concentration principle (valid for a large class of resampling weights) and the second on a direct resampled quantile, specifically using Rademacher weights. Several intermediate results established in the approach based on concentration principles are of self-interest. We also discuss the question of accuracy when using Monte-Carlo approximations of the resampled quantities. We present an application of these results to the one-sided and two-sided multiple testing problem, in which we derive several resampling-based step-down procedures providing a non-asymptotic FWER control. We compare our different procedures in a simulation study, and we show that they can outperform Bonferroni's or Holm's procedures as soon as the observed vector has sufficiently correlated coordinates.

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