pairwise rotation invariant co occurrence local binary pattern designing effective features is a fundamental problem in computer vision however it is usually difficult to achieve a great tradeoff between discriminative power and robustness previous works shown that spatial co occurrence can boost the discriminative power of features however the current existing co occurrence features are taking few considerations to the robustness and hence suffering from sensitivity to geometric and photometric variations in this work we study the transform invariance ti of co occurrence features concretely we formally introduce a pairwise transform invariance pti principle and then propose a novel pairwise rotation invariant co occurrence local binary pattern pricolbp feature and further extend it to incorporate multi scale multi orientation and multi channel information different from other lbp variants pricolbp can not only capture the spatial context co occurrence information effectively but also possess rotation invariance we evaluate pricolbp comprehensively on nine benchmark data sets from five different perspectives e g encoding strategy rotation invariance the number of templates speed and discriminative power compared to other lbp variants furthermore we apply pricolbp to six different but related applications texture material flower leaf food and scene classification and demonstrate that pricolbp is efficient effective and of a well balanced tradeoff between the discriminative power and robustness

inference for multiple change points in time series via likelihood ratio scan statistics summary we propose a likelihood ratio scan method for estimating multiple change points in piecewise stationary processes using scan statistics reduces the computationally infeasible global multiple change point estimation problem to a number of single change point detection problems in various local windows the computation can be efficiently performed with order o npt log n consistency for the estimated numbers and locations of the change points are established moreover a procedure is developed for constructing confidence intervals for each of the change points simulation experiments and real data analysis are conducted to illustrate the efficiency of the likelihood ratio scan method

most recent changepoint detection in panel data detecting recent changepoints in time series can be important for short term prediction as we can then base predictions just on the data since the changepoint in many applications we have panel

texture information in run length matrices we use a multilevel dominant eigenvector estimation algorithm to develop a new run length texture feature extraction algorithm that preserves much of the texture information in run length matrices and significantly improves image classification accuracy over traditional run length techniques the advantage of this approach is demonstrated experimentally by the classification of two texture data sets comparisons with other methods demonstrate that the run length matrices contain great discriminatory information and that a good method of extracting such information is of paramount importance to successful classification

on conditioning a random walk to stay nonnegative let s be a real valued random walk that does not drift to so p s k 0 for all k 0 we condition s to exceed n before hitting the negative halfline respectively to stay nonnegative up to time n we study under various hypotheses the convergence ofthese conditional laws as n first when s oscillates the two approximations converge to the same probability law this feature may be lost when s drifts to specifically under suitable assumptions on the upper tail of the step distribution the two approximations then converge to different probability laws

capturing the distributional behaviour of the maximum likelihood estimator of a changepoint we consider the problem of estimating the unknown changepoint in a sequence of time ordered observations upper and lower bounds are derived for the asymptotic distribution of the maximum likelihood estimator and methods of approximation are suggested a computationally efficient algorithm is presented for deriving the bounds and approximations for the asymptotic probabilities of the maximum likelihood estimator when the parameters before and after the changepoint are unknown we also show an essentially exponential rate of convergence of the probability distribution of the maximum likelihood estimator from finite samples to the case of infinite samples we apply the algorithm to the cases of normal and exponential distributions for the exponential distribution the lower and upper bounds for the right tail probabilities of the maximum likelihood estimator and the two approximations are identical this is not the case for the normal distribution finally we apply our changepoint analysis for the case of the exponential distribution to data on explosions in british coal mines keywords maximum likelihood estimator maximum of a random walk negative drift parameter change

weiss weinstein bound on multiple change points estimation in the context of multiple change points estimation performance analysis of estimators such as the maximum likelihood is often difficult to assess since the regularity assumptions are not met focusing on the estimators variance one can however use lower bounds on the mean square error in this paper we derive the so called weiss weinstein bound wwb that is known to be an efficient tool in signal processing to obtain a fair overview of the estimation behavior contrary to several works about performance analysis in the change point literature our study is adapted to multiple changes first useful formulas are given for a general estimation problem whatever the considered distribution of the data second closed form expressions are given in the cases of gaussian observations with changes in the mean and or the variance and changes in the mean rate of a poisson distribution furthermore a semidefinite programming formulation of the minimization procedure is given in order to compute the tightest wwb specifically it consists of finding the unique minimum volume covering the set constituted by hyperellipsoid elements that are generated using the derived candidate wwb matrices w r t the so called loewner partial ordering finally simulation results are provided to show the good behavior of the proposed bound

fisher tensors for classifying human epithelial cells analyzing and classifying human epithelial type 2 hep 2 cells using indirect immunofluorescence protocol has been the golden standard for detecting connective tissue diseases such as rheumatoid arthritis however this suffers from numerous shortcomings such as being subjective as well as time and labor intensive recently several studies explore the advantages of artificial systems to automate the process not only to reduce the test turn around time but also to deliver more consistent results in this paper we extend the conventional bag of word models from euclidean space to non euclidean riemannian manifolds and utilize them to classify the hep 2 cells the main motivation comes from the observation that hep 2 cells can be efficiently described by symmetric positive definite matrices which lie on a riemannian manifold with this motivation we first discuss an intrinsic bag of riemannian words model we then propose fisher tensors which can in turn encode additional information about the distribution of the signatures in a bag of word model experiments on two challenging hep 2 images datasets namely icprcontest and snphep 2 show that the proposed methods obtain notable improvements in discrimination accuracy in comparison to baseline and several state of the art methods the proposed framework while hand crafted towards cell classification is a generic framework for object recognition this is supported by assessing the performance of our proposal on a challenging texture classification task

confidence regions and tests for a change point in a sequence of exponential family random variables summary maximum likelihood methods are used to test for a change in a sequence of independent exponential family random variables with particular emphasis on the exponential distribution the exact null and alternative distributions of the test statistics are found and the power is compared with a test based on a linear trend statistic exact and approximate confidence regions for the change point are based on the values accepted by a level x likelihood ratio test and a modification of the method proposed by cox spj0tvoll 1982 the methods are applied to a classical data set on the time intervals between coal mine explosions and the change in variation of stock market returns in both cases the confidence regions for the change point cover historical events that may have caused the changes

a statistical approach to texture classification from single images we investigate texture classification from single images obtained under unknown viewpoint and illumination a statistical approach is developed where textures are modelled by the joint probability distribution of filter responses this distribution is represented by the frequency histogram of filter response cluster centres textons recognition proceeds from single uncalibrated images and the novelty here is that rotationally invariant filters are used and the filter response space is low dimensional classification performance is compared with the filter banks and methods of leung and malik ijcv 2001 schmid cvpr 2001 and cula and dana ijcv 2004 and it is demonstrated that superior performance is achieved here classification results are presented for all 61 materials in the columbia utrecht texture database we also discuss the effects of various parameters on our classification algorithm such as the choice of filter bank and rotational invariance the size of the texton dictionary as well as the number of training images used finally we present a method of reliably measuring relative orientation co occurrence statistics in a rotationally invariant manner and discuss whether incorporating such information can enhance the classifier s performance

the structure of locally orderless images we propose a representation of images in which a global but not a local topology is defined the topology is restricted to resolutions up to the extent of the local region of interest roi although the roi s may contain many pixels there is no spatial order on the pixels within the roi the only information preserved is the histogram of pixel values within the roi s this can be considered as an extreme case of a textel texture element image the histogram is the limit of texture where the spatial order has been completely disregarded we argue that locally orderless images are ubiquitous in perception and the visual arts formally the orderless images are most aptly described by three mutually intertwined scale spaces the scale parameters correspond to the pixellation inner scale the extent of the roi s outer scale and the resolution in the histogram tonal scale we describe how to construct locally orderless images how to render them and how to use them in a variety of local and global image processing operations

random walks in cones we study the asymptotic behavior of a multidimensional random walk in a general cone we find the tail asymptotics for the exit time and prove integral and local limit theorems for a random walk conditioned to stay in a cone the main step in the proof consists in constructing a positive harmonic function for our random walk under minimal moment restrictions on the increments for the proof of tail asymptotics and integral limit theorems we use a strong approximation of random walks by brownian motion for the proof of local limit theorems we suggest a rather simple approach which combines integral theorems for random walks in cones with classical local theorems for unrestricted random walks we also discuss some possible applications of our results to ordered random walks and lattice path enumeration

inferences for the linear errors in variables with changepoint models abstract a new linear structural errors in variables regression with changepoint model is considered in this model we consider the likelihood ratio test based on the maximum hotelling t 2 for the test of no change against the alternative of exactly one change if there is a change either known a priori or by testing then we estimate the unknown changepoint parameter and some other related parameters by maximum likelihood the limiting distribution of the changepoint estimator is investigated and it is shown that the asymptotic efficiency increases as the absolute regression slope coefficient increases a monte carlo study shows that the proposed estimator performs satisfactorily

estimation and inference in unstable nonlinear least squares models there is compelling evidence that many macroeconomic and financial variables are not generated by linear models this evidence is based on testing linearity against either smooth nonlinearity or piece wise linearity but there is no framework that encompasses both this paper provides an econometric framework that allows for both breaks and smooth nonlinearity in between breaks we estimate the unknown break dates simultaneously with other parameters via nonlinear least squares using new central limit results for nonlinear processes we provide inference methods on break dates and parameter estimates and several instability tests we illustrate our methods via simulated and empirical smooth transition models with breaks

logistic regression with brownian like predictors this article introduces a new type of logistic regression model involving functional predictors of binary responses and provides an extension of this approach to generalized linear models the predictors are trajectories that have certain sample path properties in common with brownian motion time points are treated as parameters of interest and confidence intervals are developed under prospective and retrospective case control sampling designs in an application to functional magnetic resonance imaging data signals from individual subjects are used to find the portion of the time course that is most predictive of the response this allows the identification of sensitive time points specific to a brain region and associated with a certain task which can be used to distinguish between responses a second application concerns gene expression data in a case control study involving breast cancer where the aim is to identify genetic loci along a chromosome that best discriminate between cases and controls

localization in log gamma polymers with boundaries consider the directed polymer in one space dimension in log gamma environment with boundary conditions introduced by seppalainen ann probab 40 1 19 73 2012 in the equilibrium case we prove that the end point of the polymer converges in law as the length increases to a density proportional to the exponent of a zero mean random walk this holds without space normalization and the mass concentrates in a neighborhood of the minimum of this random walk we have analogous results out of equilibrium as well as for the middle point of the polymer with both ends fixed the existence and the identification of the limit relies on the analysis of a random walk seen from its infimum

river stream flows in the northern quebec labrador region a multivariate change point analysis via maximum likelihood 1 spring stream flows of six rivers that flow in the northern quebec labrador region are modeled in a multivariate gaussian framework and have been analyzed for possible change points in their average flows the multivariate formulation takes into account correlations among the flows of the six rivers that flow in the same region significant change was detected in the multivariate mean vector and the unknown change point is estimated by the method of maximum likelihood estimation we then establish the asymptotic distribution of the change point maximum likelihood estimate wherein we show that the multivariate case can be transformed into an equivalent univariate problem a simulation study is carried out to investigate the robustness of the asymptotic distribution to departures from normality and independence as well as its closeness to finite samples the asymptotic distribution allowed us to compute confidence interval estimates of the change point in the river flows a decrease in the mean river flows in 1984 identified by the methodology may have been a consequence of a sharp decline in the region s snow cover that occurred about a year or two ahead

a performance evaluation of local descriptors in this paper we compare the performance of descriptors computed for local interest regions as for example extracted by the harris affine detector mikolajczyk k and schmid c 2004 many different descriptors have been proposed in the literature it is unclear which descriptors are more appropriate and how their performance depends on the interest region detector the descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the detector our evaluation uses as criterion recall with respect to precision and is carried out for different image transformations we compare shape context belongie s et al april 2002 steerable filters freeman w and adelson e setp 1991 pca sift ke y and sukthankar r 2004 differential invariants koenderink j and van doorn a 1987 spin images lazebnik s et al 2003 sift lowe d g 1999 complex filters schaffalitzky f and zisserman a 2002 moment invariants van gool l et al 1996 and cross correlation for different types of interest regions we also propose an extension of the sift descriptor and show that it outperforms the original method furthermore we observe that the ranking of the descriptors is mostly independent of the interest region detector and that the sift based descriptors perform best moments and steerable filters show the best performance among the low dimensional descriptors

local behaviour of first passage probabilities suppose that s is an asymptotically stable random walk with norming sequence cn and that tx is the time that s first enters x where x 0 the asymptotic behaviour of p t0 n has been described in a recent paper of vatutin and wachtel probab theory relat fields 143 177 217 2009 and here we build on that result to give three estimates for p tx n which hold uniformly as n in the regions x o cn x o cn and x cn respectively

nonparametric inference on structural breaks this paper proposes estimators of location and size of structural breaks in a possibly dynamic nonparametric regression model the structural breaks can be located at given periods of time and or they can be explained by the values taken by some regressor as in threshold models no previous knowledge of the underlying regression function is required the paper also studies the case in which several regressors explain the breaks we derive the rate of convergence and provide central limit theorems for the estimators of the location s and size s a monte carlo experiment illustrates the performance of our estimators in small samples

on convergence properties of the em algorithm for gaussian mixtures we build up the mathematical connection between the expectation maximization em algorithm and gradient based approaches for maximum likelihood learning of finite gaussian mixtures we show that the em step in parameter space is obtained from the gradient via a projection matrix p and we provide an explicit expression for the matrix we then analyze the convergence of em in terms of special properties of p and provide new results analyzing the effect that p has on the likelihood surface based on these mathematical results we present a comparative discussion of the advantages and disadvantages of em and other algorithms for the learning of gaussian mixture models

criticality for branching processes in random environment we study branching processes in an i i d random environment where the associated random walk is of the oscillating type this class of processes generalizes the classical notion of criticality the main properties of such branching processes are developed under a general assumption known as spitzer s condition in fluctuation theory of random walks and some additional moment condition we determine the exact asymptotic behavior of the survival probability and prove conditional functional limit theorems for the generation size process and the associated random walk the results rely on a stimulating interplay between branching process theory and fluctuation theory of random walks

estimating structural changes in regression quantiles this paper considers the estimation of multiple structural changes occurring at unknown dates in one or multiple conditional quantile functions the analysis covers time series models as well as models with repeated cross sections we estimate the break dates and other parameters jointly by minimizing the check function over all permissible break dates the limiting distribution of the estimator is derived and the coverage property of the resulting confidence interval is assessed via simulations a procedure to determine the number of breaks is also discussed empirical applications to the quarterly us real gdp growth rate and the underage drunk driving data suggest that the method can deliver more informative results than the analysis of the conditional mean function alone

an invariance principle to ferrari spohn diffusions we prove an invariance principle for a class of tilted 1 1 dimensional sos models or equivalently for a class of tilted random walk bridges in mathbb z the limiting objects are stationary reversible ergodic diffusions with drifts given by the logarithmic derivatives of the ground states of associated singular sturm liouville operators in the case of a linear area tilt we recover the ferrari spohn diffusion with log airy drift which was derived in ferrari and spohn ann probab 33 4 1302 1325 2005 in the context of brownian motions conditioned to stay above circular and parabolic barriers

invariance principles for local times at the maximum of random walks and levy processes we prove that when a sequence of levy processes x n or a normed sequence of random walks s n converges a s on the skorokhod space toward a levy process x the sequence l n of local times at the supremum of x n converges uniformly on compact sets in probability toward the local time at the supremum of x a consequence of this result is that the sequence of quadrivariate ladder processes both ascending and descending converges jointly in law towards the ladder processes of x as an application we show that in general the sequence s n conditioned to stay positive converges weakly jointly with its local time at the future minimum towards the corresponding functional for the limiting process x from this we deduce an invariance principle for the meander which extends known results for the case of attraction to a stable law

hep 2 cell classification using shape index histograms with donut shaped spatial pooling we present a new method for automatic classification of indirect immunoflourescence images of hep 2 cells into different staining pattern classes our method is based on a new texture measure called shape index histograms that captures second order image structure at multiple scales moreover we introduce a spatial decomposition scheme which is radially symmetric and suitable for cell images the spatial decomposition is performed using donut shaped pooling regions of varying sizes when gathering histogram contributions we evaluate our method using both the icip 2013 and the icpr 2012 competition datasets our results show that shape index histograms are superior to other popular texture descriptors for hep 2 cell classification moreover when comparing to other automated systems for hep 2 cell classification we show that shape index histograms are very competitive especially considering the relatively low complexity of the method

nonparametric estimation of structural change points in volatility models for time series we propose a hybrid estimation procedure that combines the least squares and nonparametric methods to estimate change points of volatility in time series models its main advantage is that it does not require any specific form of marginal or transitional densities of the process we also establish the asymptotic properties of the estimators when the regression and conditional volatility functions are not known the proposed tests for change points of volatility are shown to be consistent and more powerful than the nonparametric ones in the literature finally we provide simulations and empirical results using the hong kong stock market index hsi series

the functional equation of the smoothing transform given a sequence t ti i 1 of non negative random variables a function f on the positive halfline can be transformed to e q i 1 f tti we study the fixed points of this transform within the class of decreasing functions by exploiting the intimate relationship with general branching processes a full description of the set of solutions is established without the moment conditions that figure in earlier studies since the class of functions under consideration contains all laplace transforms of probability distributions on 0 1 the results provide the full description of the set of solutions to the fixed point equation of the smoothing transform x d

multiresolution gray scale and rotation invariant texture classification with local binary patterns presents a theoretically very simple yet efficient multiresolution approach to gray scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions the method is based on recognizing that certain local binary patterns termed uniform are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature we derive a generalized gray scale and rotation invariant operator presentation that allows for detecting the uniform patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis the proposed approach is very robust in terms of gray scale variations since the operator is by definition invariant against any monotonic transformation of the gray scale another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns

new distribution theory for the estimation of structural break point in mean abstract based on the girsanov theorem this paper obtains the exact distribution of the maximum likelihood estimator of structural break point in a continuous time model the exact distribution is asymmetric and tri modal indicating that the estimator is biased these two properties are also found in the finite sample distribution of the least squares ls estimator of structural break point in the discrete time model suggesting the classical long span asymptotic theory is inadequate the paper then builds a continuous time approximation to the discrete time model and develops an in fill asymptotic theory for the ls estimator the in fill asymptotic distribution is asymmetric and tri modal and delivers good approximations to the finite sample distribution to reduce the bias in the estimation of both the continuous time and the discrete time models a simulation based method based on the indirect estimation ie approach is proposed monte carlo studies show that ie achieves substantial bias reductions

hep 2 staining pattern recognition using stacked fisher network for encoding weber local descriptor abstract this study addresses the recognition problem of the hep 2 cell using indirect immunofluorescent iif image analysis which can indicate the presence of autoimmune diseases by finding antibodies in the patient serum generally the method used for iif analysis remains subjective and depends too heavily on the experience and expertise of the physician this study aims to explore an automatic hep 2 cell recognition system in which how to extract highly discriminate visual features plays a key role in this recognition application in order to realize this purpose our main efforts include 1 a simple but robust local descriptor without any quantization for local patch representation 2 a transformation of the difference values between the surrounding pixels and the center one to the perception degree which is based on the fact that human perception for disguising a pattern depends not only on the absolute intensity of the stimulus but also on the relative variance of the stimulus called as weber local descriptor wld 3 a data driven coding strategy with a parametric probability process and the extraction of not only low but also high order statistics for image representation called as fisher vector 4 the stacking of the fisher network into multi layer framework for more discriminate feature experiments using the open hep 2 cell dataset released in the icip2013 contest validate that the proposed strategy can achieve a much better performance than the state of the art approaches and that the achieved recognition error rate is even very significantly below the observed intra laboratory variability

hep 2 cell image classification with multiple linear descriptors abstract the automatic classification of the hep 2 cell stain patterns from indirect immunofluorescence images has attracted much attention recently as an image classification problem it can be well solved by the state of the art bag of features bof model as long as a suitable local descriptor is known unfortunately for this special task we have very limited knowledge of such a descriptor in this paper we explore the possibility of automatically learning the descriptor from the image data itself specifically we assume that a local patch can be well described by a set of linear projections performed on its pixel values based on this assumption both unsupervised and supervised approaches are explored for learning the projections more importantly we propose a multi projection multi codebook scheme which creates multiple linear projection descriptors and multiple image representation channels with each channel corresponding to one descriptor through our analysis we show that the image representation obtained by combining these different channels can be more discriminative than that obtained from a single projection scheme this analysis is further verified by our experimental study we evaluate the proposed approach by strictly following the protocol suggested by the organizer of the 2012 hep 2 cell classification contest which is hosted to compare the state of the art methods for hep 2 cell classification in this paper our system achieves 66 6 cell level classification accuracy which is just slightly lower than the best performance achieved in the hep 2 cell classification contest this result is impressive and promising considering that we only utilize a single type of feature namely linear projection coefficients of patch pixel values which is learned from the image data

sample splitting and threshold estimation threshold models have a wide variety of applications in economics direct applications include models of separating and multiple equilibria other applications include empirical sample splitting when the sample split is based on a continuously distributed variable such as firm size in addition threshold models may be used as a parsimonious strategy for nonparametric function estimation for example the threshold autoregressive model tar is popular in the nonlinear time series literature threshold models also emerge as special cases of more complex statistical frameworks such as mixture models switching models markov switching models and smooth transition threshold models it may be important to understand the statistical properties of threshold models as a preliminary step in the development of statistical tools to handle these more complicated structures despite the large number of potential applications the statistical theory of threshold estimation is undeveloped it is known that threshold estimates are super consistent but a distribution theory useful for testing and inference has yet to be provided this paper develops a statistical theory for threshold estimation in the regression context we allow for either cross section or time series observations least squares estimation of the regression parameters is considered an asymptotic distribution theory for the regression estimates the threshold and the regression slopes is developed it is found that the distribution of the threshold estimate is nonstandard a method to construct asymptotic confidence intervals is developed by inverting the likelihood ratio statistic it is shown that this yields asymptotically conservative confidence regions monte carlo simulations are presented to assess the accuracy of the asymptotic approximations the empirical relevance of the theory is illustrated through an application to the multiple equilibria growth model of durlauf and johnson 1995

efficient k nn based hep 2 cells classifier human epithelial hep 2 cells are commonly used in the indirect immunofluorescence iif tests to detect autoimmune diseases the diagnosis consists of searching and classification to specific patterns created by anti nuclear antibodies anas in the patient serum evaluation of the iif test is mostly done by humans which means that it is highly dependent on the experience and expertise of the physician therefore a significant amount of research has been focused on the development of computer aided diagnostic systems which could help with the analysis of images from microscopes this work deals with the design and development of hep 2 cells classifier the classifier is able to categorize pre segmented images of hep 2 cells into 6 classes the core of this engine consists of the following image descriptors haralick features local binary patterns sift surface description and a granulometry based descriptor these descriptors produce vectors that form metric spaces k nn classification is based on aggregated distance function which combines several features together an extensive set of evaluations was performed on the publicly available mivia hep 2 images dataset which allows a direct comparison of our approach with other solutions the results show that our approach is one of the leading classifiers when comparing with other participants in the hep 2 cells classification contest

path decompositions for markov chains we present two path decompositions of markov chains with general state space by means of harmonic functions which are dual to each other they can be seen as generalization of williams decomposition of a brownian motion with drift 1973 the results may be illustrated by a multitude of examples we confine ourselves to dierent types of

common breaks in means and variances for panel data this paper establishes the consistency of the estimated common break point in panel data consistency is obtainable even when a regime contains a single observation making it possible to quickly identify the onset of a new regime we also propose a new framework for developing the limiting distribution for the estimated break point and show how to construct confidence intervals the least squares method is used for estimating breaks in means and the quasi maximum likelihood qml method is used to estimate breaks in means and in variances qml is shown to be more efficient than the least squares even if there is no change in the variances

using basic image features for texture classification representing texture images statistically as histograms over a discrete vocabulary of local features has proven widely effective for texture classification tasks images are described locally by vectors of for example responses to some filter bank and a visual vocabulary is defined as a partition of this descriptor response space typically based on clustering in this paper we investigate the performance of an approach which represents textures as histograms over a visual vocabulary which is defined geometrically based on the basic image features of griffin and lillholm proc spie 6492 09 1 11 2007 rather than by clustering bifs provide a natural mathematical quantisation of a filter response space into qualitatively distinct types of local image structure we also extend our approach to deal with intra class variations in scale our algorithm is simple there is no need for a pre training step to learn a visual dictionary as in methods based on clustering and no tuning of parameters is required to deal with different datasets we have tested our implementation on three popular and challenging texture datasets and find that it produces consistently good classification results on each including what we believe to be the best reported for the kth tips and equal best reported for the uiuctex databases

inference regarding multiple structural changes in linear models with endogenous regressors this paper considers the linear model with endogenous regressors and multiple changes in the parameters at unknown times it is shown that minimization of a generalized method of moments criterion yields inconsistent estimators of the break fractions but minimization of the two stage least squares 2sls criterion yields consistent estimators of these parameters we develop a methodology for estimation and inference of the parameters of the model based on 2sls the analysis covers the cases where the reduced form is either stable or unstable the methodology is illustrated via an application to the new keynesian phillips curve for the us

improving bag of features for large scale image search this article improves recent methods for large scale image search we first analyze the bag of features approach in the framework of approximate nearest neighbor search this leads us to derive a more precise representation based on hamming embedding he and weak geometric consistency constraints wgc he provides binary signatures that refine the matching based on visual words wgc filters matching descriptors that are not consistent in terms of angle and scale he and wgc are integrated within an inverted file and are efficiently exploited for all images in the dataset we then introduce a graph structured quantizer which significantly speeds up the assignment of the descriptors to visual words a comparison with the state of the art shows the interest of our approach when high accuracy is needed experiments performed on three reference datasets and a dataset of one million of images show a significant improvement due to the binary signature and the weak geometric consistency constraints as well as their efficiency estimation of the full geometric transformation i e a re ranking step on a short list of images is shown to be complementary to our weak geometric consistency constraints our approach is shown to outperform the state of the art on the three datasets

planar stochastic hyperbolic triangulations pursuing the approach of angel and ray ann probab 2015 we introduce and study a family of random infinite triangulations of the full plane that satisfy a natural spatial markov property these new random lattices naturally generalize angel and schramm s uniform infinite planar triangulation uipt and are hyperbolic in flavor we prove that they exhibit a sharp exponential volume growth are non liouville and that the simple random walk on them has positive speed almost surely we conjecture that these infinite triangulations are the local limits of uniform triangulations whose genus is proportional to the size an artistic representation of a random 3 connected triangulation of the plane with hyperbolic flavor

fractals with point impact in functional linear regression this paper develops a point impact linear regression model in which the trajectory of a continuous stochastic process when evaluated at a sensitive time point is associated with a scalar response the proposed model complements and is more interpretable than the functional linear regression approach that has become popular in recent years the trajectories are assumed to have fractal self similar properties in common with a fractional brownian motion with an unknown hurst exponent bootstrap confidence intervals based on the least squares estimator of the sensitive time point are developed misspecification of the point impact model by a functional linear model is also investigated non gaussian limit distributions and rates of convergence determined by the hurst exponent play an important role

τ estimators of regression models with structural change of unknown location abstract this paper concerns with robust estimation of linear regression models with structural change of unknown location under possibly contaminated distributions both for the regressors and for the perturbance term existing estimators will be inefficient in this context furthermore they may protect against outlying yt but cannot cope with leverage points namely outliers in the factor space which could have large influence on the fit as a result these estimators could not discriminate between outlier observations and structural break points misplacing the shift location this fact can be of special importance in practice therefore it may be advisable to consider robust estimators under possible leverage points in a structural change context thus we propose the estimator introduced by yohai and zamar j am stat assoc 83 1988 406 in the standard context of no change this type of estimator is qualitatively robust with the best possible breakdown point and highly efficient under normal errors the asymptotic distribution of the break location estimator is obtained both for fixed magnitude of shift and for shift with magnitude converging to zero as the sample size increases the analysis is carried out in the framework of general ned dependence conditions for the data monte carlo experiments illustrates the performance of our estimators in finite samples

invariant measures for the box ball system based on stationary markov chains and periodic gibbs measures the box ball system bbs is a simple model of soliton interaction introduced by takahashi and satsuma in the 1990s recent work of the authors together with tsuyoshi kato and satoshi tsujimoto derived various families of invariant measures for the bbs based on two sided stationary markov chains d a croydon et al dynamics of the box ball system with random initial conditions via pitman s transformation arxiv 1806 02147 in this article we survey the invariant measures that were presented in d a croydon et al and also introduce a family of new ones for periodic configurations that are expressed in terms of gibbs measures moreover we show that the former examples can be obtained as infinite volume limits of the latter another aspect of d a croydon et al was to describe scaling limits for the bbs here we review the results of d a croydon et al and also present scaling limits other than those that were covered there one the zigzag process has previously been observed in the context of queuing another a periodic version of the zigzag process is apparently novel furthermore we demonstrate that certain palm measures associated with the stationary and periodic versions of the zigzag process yield natural invariant measures for the dynamics of corresponding versions of the ultradiscrete toda lattice the box ball system bbs is a simple model of soliton interaction introduced by takahashi and satsuma in the 1990s recent work of the authors together with tsuyoshi kato and satoshi tsujimoto derived various families of invariant measures for the bbs based on two sided stationary markov chains d a croydon et al dynamics of the box ball system with random initial conditions via pitman s transformation arxiv 1806 02147 in this article we survey the invariant measures that were presented in d a croydon et al and also introduce a family of new ones for periodic configurations that are expressed in terms of gibbs measures moreover we show that the former examples can be obtained as infinite volume limits of the latter another aspect of d a croydon et al was to describe scaling limits for the bbs here we review the results of d a croydon et al and also present scaling limits other than those that were covered there one the zigzag process has previously been observed in the context

hep 2 cell classification using rotation invariant co occurrence among local binary patterns abstract this paper proposes a novel method for classifying six categories of patterns of fluorescence staining of a hep 2 cell the proposed method is constructed as a combination of the powerful rotation invariant co occurrence among adjacent local binary pattern ric lbp image feature and a linear support vector machine svm ric lbp provides high descriptive ability and robustness against local rotations of an input cell image to further deal with global rotation we synthesize many training images by rotating the original training images and constructing the svm using both the original and synthesized images the proposed method has the following advantages 1 robustness against uniform changes in intensity of an input cell image 2 invariance under local and global rotation of the image 3 low computational cost and 4 easy implementation the proposed method was demonstrated to be effective through evaluation experiments using the mivia hep 2 images dataset and comparison with typical state of the art methods

conditioned local limit theorems for random walks defined on finite markov chains let x n n geqslant 0 be a markov chain with values in a finite state space mathbb x starting at x 0 x in mathbb x and let f be a real function defined on mathbb x set s n sum k 1 n f x k n geqslant 1 for any y in mathbb r denote by tau y the first time when y s n becomes non positive we study the asymptotic behaviour of the probability mathbb p x left y s n in z z a tau y n right as n rightarrow infty we first establish for this probability a conditional version of the local limit theorem of stone then we find for it an asymptotic equivalent of order n 3 2 and give a generalization which is useful in applications we also describe the asymptotic behaviour of the probability mathbb p x left tau y n right as n rightarrow infty

a general regression changepoint test for time series data abstractthis article develops a test for a single changepoint in a general setting that allows for correlated time series regression errors a seasonal cycle time varying regression factors and covariate information within a changepoint statistic is constructed from likelihood ratio principles and its asymptotic distribution is derived the asymptotic distribution of the changepoint statistic is shown to be invariant of the seasonal cycle and the covariates should the latter obey some simple limit laws however the limit distribution depends on any time varying factors a new test based on arma residuals is developed and is shown to have favorable properties with finite samples driving our work is a changepoint analysis of the mauna loa record of monthly carbon dioxide concentrations this series has a pronounced seasonal cycle a nonlinear trend heavily correlated regression errors and covariate information in the form of climate oscillations in the end we find a prominent changepoint in the ea

exploring illumination robust descriptors for human epithelial type 2 cell classification strong illumination variation is a key challenge in the human epithelial type 2 hep 2 cell classification task aiming to improve the robustness of the hep 2 classification system to the illumination variation this paper deeply explores discriminative and illumination robust descriptors specifically we propose a novel spatial shape index descriptor ssid to capture spatial layout information of the second order structures and utilize a local orientation adaptive descriptor load which was originally designed for texture classification to the hep 2 cell classification task both ssid and load show strong discrimination and great complementarity to each other four different sets of experiments were carried out to evaluate ssid load and their combination our two submissions achieved superior performance on the new executable thematic of pattern recognition techniques for indirect immunofluorescence images analysis compared to the rank 1st method in the icpr 2014 hep 2 cell classification contest both of our submissions achieved a better performance when only using the provided training data our approaches also demonstrated superior performance on a newly compiled large scale hep 2 data set with 63 445 cell images highlightstwo introduced features achieve superior performance on the hep 2 cell classification task we propose a novel spatial shape index descriptor ssid to capture spatial layout of the second order texture structures we utilize a multi scale local orientation adaptive descriptor load to the hep 2 cell classification task we introduce a new large scale hep 2 data set that contains 63 445 cell images from the i3a task 2 data set

martingales in self similar growth fragmentations and their connections with random planar maps the purpose of the present work is twofold first we develop the theory of general self similar growth fragmentation processes by focusing on martingales which appear naturally in this setting and by recasting classical results for branching random walks in this framework in particular we establish many to one formulas for growth fragmentations and define the notion of intrinsic area of a growth fragmentation second we identify a distinguished family of growth fragmentations closely related to stable levy processes which are then shown to arise as the scaling limit of the perimeter process in markovian explorations of certain random planar maps with large degrees which are roughly speaking the dual maps of the stable maps of le gall and miermont in ann probab 39 1 69 2011 as a consequence of this result we are able to identify the law of the intrinsic area of these distinguished growth fragmentations this generalizes a geometric connection between large boltzmann triangulations and a certain growth fragmentation process which was established in bertoin et al ann probab accepted