Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the usage of dockless bike sharing in Singapore

ABSTRACT A new generation of bike-sharing services without docking stations is currently revolutionizing the traditional bike-sharing market as it dramatically expands around the world. This study aims at understanding the usage of new dockless bike-sharing services through the lens of Singapore's prevalent service. We collected the GPS data of all dockless bikes from one of the largest bike sharing operators in Singapore for nine consecutive days, for a total of over 14 million records. We adopted spatial autoregressive models to analyze the spatiotemporal patterns of bike usage during the study period. The models explored the impact of bike fleet size, surrounding built environment, access to public transportation, bicycle infrastructure, and weather conditions on the usage of dockless bikes. Larger bike fleet is associated with higher usage but with diminishing marginal impact. In addition, high land use mixtures, easy access to public transportation, more supportive cycling facilities, and free-ride promotions positively impact the usage of dockless bikes. The negative influence of rainfall and high temperatures on bike utilization is also exhibited. The study also offered some guidance to urban planners, policy makers, and transportation practitioners who wish to promote bike-sharing service while ensuring its sustainability.
Possible Evidence For Dark Matter Annihilation In The Inner Milky Way From The Fermi Gamma Ray Space Telescope

We study the gamma rays observed by the Fermi Gamma Ray Space Telescope from the direction of the Galactic Center and find that their angular distribution and energy spectrum are well described by a dark matter annihilation scenario. In particular, we find a good fit to the data for dark matter particles with a 25-30 GeV mass, an annihilation cross section of ~9x10^-26 cm^3/s, and that are distributed with a cusped halo profile within the inner kiloparsec of the Galaxy. We cannot, however, exclude the possibility that these photons originate from an astrophysical source or sources with a similar morphology and spectral shape to those predicted in an annihilating dark matter scenario.
ESTIMATING THE PARAMETER OF A BIASED COIN ∗

This paper deals with the application of approximation theory techniques to study a classical problem in probability: estimating the parameter of a biased coin. For this purpose, a minimax estimation problem is considered, and the characterization of the optimal estimator is shown together with the weak asymptotics of such optimal choices as the number of coin tosses approaches infinity. In addition, a number of numerical examples and graphs are provided. At the same time, the problem is also discussed from the game theory viewpoint, as a non-cooperative, two-player game, and the existence of a Nash-equilibrium is established. The particular case of n = 2 tosses is completely solved.
CARMA Processes driven by Non-Gaussian Noise

We present an outline of the theory of certain L\'evy-driven, multivariate stochastic processes, where the processes are represented by rational transfer functions (Continuous-time AutoRegressive Moving Average or CARMA models) and their applications in non-Gaussian time series modelling. We discuss in detail their definition, their spectral representation, the equivalence to linear state space models and further properties like the second order structure and the tail behaviour under a heavy-tailed input. Furthermore, we study the estimation of the parameters using quasi-maximum likelihood estimates for the auto-regressive and moving average parameters, as well as how to estimate the driving L\'evy process.
New consistent and asymptotically normal estimators for random graph mixture models

Random graph mixture models are now very popular for modeling real data networks. In these setups, parameter estimation procedures usually rely on variational approximations, either combined with the expectation-maximisation (\textsc{em}) algorithm or with Bayesian approaches. Despite good results on synthetic data, the validity of the variational approximation is however not established. Moreover, the behavior of the maximum likelihood or of the maximum a posteriori estimators approximated by these procedures is not known in these models, due to the dependency structure on the variables. In this work, we show that in many different affiliation contexts (for binary or weighted graphs), estimators based either on moment equations or on the maximization of some composite likelihood are strongly consistent and $\sqrt{n}$-convergent, where $n$ is the number of nodes. As a consequence, our result establishes that the overall structure of an affiliation model can be caught by the description of the network in terms of its number of triads (order 3 structures) and edges (order 2 structures). We illustrate the efficiency of our method on simulated data and compare its performances with other existing procedures. A data set of cross-citations among economics journals is also analyzed.
Revealing Sub-Optimality Conditions of Strategic Decisions

Conceptual view of fitness and fitness measurement of strategic decisions on information systems, technological systems and innovation are becoming more important in recent years. This paper determines some dynamics of fitness landscape which are lead to termination of decision makers' research before reaching the global maximum in strategic decisions. These dynamics are specified according to management decision making models and supported with simulation results. This article determines simulation results by means of "Fitness Value" and "Probability of Optimality". Correlation between these two concepts may be remarkable according to revealing optimal values in innovative and research-based decision making approaches beside sub-optimal results of traditional decision making approaches.
On the occurrence of large gaps in small contingency tables

Examples of small contingency tables on binary random variables with large integer programming gaps on the lower bounds of cell entries were constructed by Sullivant. We argue here that the margins for which these constructed large gaps occur are rarely encountered, thus reopening the question of whether linear programming is an effective heuristic for detecting disclosures when releasing margins of multi-way tables. The notion of ``rarely encountered'' is made precise through the language of standard pairs.
Multicategory vertex discriminant analysis for high-dimensional data

In response to the challenges of data mining, discriminant analysis continues to evolve as a vital branch of statistics. Our recently introduced method of vertex discriminant analysis (VDA) is ideally suited to handle multiple categories and an excess of predictors over training cases. The current paper explores an elaboration of VDA that conducts classification and variable selection simultaneously. Adding lasso (l-norm) and Euclidean penalties to the VDA loss function eliminates unnecessary predictors. Lasso penalties apply to each predictor coefficient separately; Euclidean penalties group the collective coefficients of a single predictor. With these penalties in place, cyclic coordinate descent accelerates estimation of all coefficients. Our tests on simulated and benchmark real data demonstrate the virtues of penalized VDA in model building and prediction in high-dimensional settings.
Multivariate Goodness of Fit Procedures for Unbinned Data: An Annotated Bibliography

Unbinned maximum likelihood is a common procedure for parameter estimation. After parameters have been estimated, it is crucial to know whether the fit model adequately describes the experimental data. Univariate Goodness of Fit procedures have been thoroughly analyzed. In multi-dimensions, Goodness of Fit test powers have rarely been studied on realistic problems. There is no definitive answer to regarding which method is better. Test performance is strictly related to specific analysis characteristics. In this work, a review of multi-variate Goodness of Fit techniques is presented.
Pair-Wise Cluster Analysis

This paper studies the problem of learning clusters which are consistently present in different (continuously valued) representations of observed data. Our setup differs slightly from the standard approach of (co-) clustering as we use the fact that some form of `labeling' becomes available in this setup: a cluster is only interesting if it has a counterpart in the alternative representation. The contribution of this paper is twofold: (i) the problem setting is explored and an analysis in terms of the PAC-Bayesian theorem is presented, (ii) a practical kernel-based algorithm is derived exploiting the inherent relation to Canonical Correlation Analysis (CCA), as well as its extension to multiple views. A content based information retrieval (CBIR) case study is presented on the multi-lingual aligned Europal document dataset which supports the above findings.
On Daryl Bem's Feeling the Future Paper

It has been argued by Daryl Bem in his 2011 paper that 8 out of 9 experiments yielded statistically significant results in favour of the psi effect. It is pointed out in this short communication that many of the results in the above mentioned paper could be explained by using well known concepts in statistics such as Confidence Level and Standard Error of the Sample Mean. This short communication also discusses implied confidence level and confidence intervals in polling results.
Convergent and Anti-diffusive Properties of Mean-Shift Method

An analytic framework based on partial differential equations is derived for certain dynamic clustering methods. The proposed mathematical framework is based on the application of the conservation law in physics to characterize successive transformations of the underlying probability density function. It is then applied to analyze the convergence and stability of mean shift type of dynamic clustering algorithms. Theoretical analysis shows that un-supervised mean-shift type of algorithm is intrinsically unstable. It is proved that the only possibility of a correct convergence for unsupervised mean shift type of algorithm is to transform the original probability density into a multivariate normal distribution with no dependence struture. Our analytical results suggest that a more stable and convergent mean shift algorithm might be achieved by adopting a judiciously chosen supervision mechanism.
A frequentist two-sample test based on Bayesian model selection

Despite their importance in supporting experimental conclusions, standard statistical tests are often inadequate for research areas, like the life sciences, where the typical sample size is small and the test assumptions difficult to verify. In such conditions, standard tests tend to be overly conservative, and fail thus to detect significant effects in the data. Here we define a novel statistical test for the two-sample problem. Several characteristics make it an attractive alternative to classical two-sample tests: 1) It is based on Bayesian model selection, and thus takes into account uncertainty about the model's parameters, mitigating the problem of small samples size; 2) The null hypothesis is compared with several alternative hypotheses, making the test suitable in different experimental scenarios; 3) The test is constructed as a frequentist test, and defines significance with the conventional bound on Type I errors. We analyze the power of the test and find that it is higher than the power of other standard options, like the t-test (up to 25% higher) for a wide range of sample and effect sizes, and is at most 1% lower when the assumptions of the t-test are perfectly matched. We discuss and evaluate two variants of the test, that define different prior distributions over the parameters of the hypotheses.
Non-Parametric Methods Applied to the N-Sample Series Comparison

Anomaly and similarity detection in multidimensional series have a long history and have found practical usage in many different fields such as medicine, networks, and finance. Anomaly detection is of great appeal for many different disciplines; for example, mathematicians searching for a unified mathematical formulation based on probability, statisticians searching for error bound estimates, and computer scientists who are trying to design fast algorithms, to name just a few. In summary, we have two contributions: First, we present a self-contained survey of the most promising methods being used in the fields of machine learning, statistics, and bio-informatics today. Included we present discussions about conformal prediction, kernels in the Hilbert space, Kolmogorov's information measure, and non-parametric cumulative distribution function comparison methods (NCDF). Second, building upon this foundation, we provide a powerful NCDF method for series with small dimensionality. Through a combination of data organization and statistical tests, we describe extensions that scale well with increased dimensionality.
Unsupervised nonparametric detection of unknown objects in noisy images based on percolation theory

We develop an unsupervised, nonparametric, and scalable statistical learning method for detection of unknown objects in noisy images. The method uses results from percolation theory and random graph theory. We present an algorithm that allows to detect objects of unknown shapes and sizes in the presence of nonparametric noise of unknown level. The noise density is assumed to be unknown and can be very irregular. The algorithm has linear complexity and exponential accuracy and is appropriate for real-time systems. We prove strong consistency and scalability of our method in this setup with minimal assumptions.
OF : A STATISTICAL ANALYSIS OF MULTIPLE TEMPERATURE PROXIES : ARE RECONSTRUCTIONS OF SURFACE TEMPERATURES OVER THE LAST 1000 YEARS RELIABLE ?

As McShane and Wyner (2011) observe, there are formidable statistical problems in “reconstructing” past temperatures from networks of so-called “proxy” data with weak “signal” and complicated autocorrelated structures. Wegman, Scott and Said (2006) regretted the combination of the lack of involvement of the statistical community with the statistical inexperience of paleoclimatologists struggling with these complicated problems. Oxburgh et al. (2010), one of the Climategate inquiries, made similar observations. Thus, the interest of McShane and Wyner in these problems is very much to be welcomed and hopefully presages wider participation by the statistical community in the very interesting problems presented by paleoclimate reconstructions.
Coherence and phase synchronization: generalization to pairs of multivariate time series, and removal of zero-lag contributions

Coherence and phase synchronization between time series corresponding to different spatial locations are usually interpreted as indicators of the connectivity between locations. In neurophysiology, time series of electric neuronal activity are essential for studying brain interconnectivity. Such signals can either be invasively measured from depth electrodes, or computed from very high time resolution, non-invasive, extracranial recordings of scalp electric potential differences (EEG: electroencephalogram) and magnetic fields (MEG: magnetoencephalogram) by means of a tomography such as sLORETA (standardized low resolution brain electromagnetic tomography). There are two problems in this case. First, in the usual situation of unknown cortical geometry, the estimated signal at each brain location is a vector with three components (i.e. a current density vector), which means that coherence and phase synchronization must be generalized to pairs of multivariate time series. Second, the inherent low spatial resolution of the EEG/MEG tomography introduces artificially high zero-lag coherence and phase synchronization. In this report, solutions to both problems are presented. Two additional generalizations are briefly mentioned: (1) conditional coherence and phase synchronization; and (2) non-stationary time-frequency analysis. Finally, a non-parametric randomization method for connectivity significance testing is outlined. The new connectivity measures proposed here can be applied to pairs of univariate EEG/MEG signals, as is traditional in the published literature. However, these calculations cannot be interpreted as connectivity, since it is in general incorrect to associate an extracranial electrode or sensor to the underlying cortex.
Higher order influence functions and minimax estimation of nonlinear functionals

We present a theory of point and interval estimation for nonlinear functionals in parametric, semi-, and non-parametric models based on higher order influence functions (Robins (2004), Section 9; Li et al. (2004), Tchetgen et al. (2006), Robins et al. (2007)). Higher order influence functions are higher order U-statistics. Our theory extends the first order semiparametric theory of Bickel et al. (1993) and van der Vaart (1991) by incorporating the theory of higher order scores considered by Pfanzagl (1990), Small and McLeish (1994) and Lindsay and Waterman (1996). The theory reproduces many previous results, produces new non-$\sqrt{n}$ results, and opens up the ability to perform optimal non-$\sqrt{n}$ inference in complex high dimensional models. We present novel rate-optimal point and interval estimators for various functionals of central importance to biostatistics in settings in which estimation at the expected $\sqrt{n}$ rate is not possible, owing to the curse of dimensionality. We also show that our higher order influence functions have a multi-robustness property that extends the double robustness property of first order influence functions described by Robins and Rotnitzky (2001) and van der Laan and Robins (2003).
CPHD filter derivation for extended targets

This document derives the CPHD filter for extended targets. Only the update step is derived here. Target generated measurements, false alarms and prior are all assumed to be independent identically distributed cluster processes. We also prove here that the derived CPHD filter for extended targets reduce to PHD filter for extended targets and CPHD filter for standard targets under suitable assumptions.
New Cell-Specific and Overall Tests of Spatial Interaction Based on Nearest Neighbor Contingency Tables

Spatial interaction patterns such as segregation and association can be tested using nearest neighbor contingency tables (NNCTs). We introduce new cell-specific (or pairwise) and overall segregation tests and determine their asymptotic distributions. In particular, we demonstrate that cell-specific tests enjoy asymptotic normality, while overall tests have chi-square distributions asymptotically. We also perform an extensive Monte Carlo simulation study to compare the finite sample performance of the tests in terms of empirical size and power. In addition to the cell-specific tests as post-hoc tests for overall tests, we discuss one-class-versus-rest type of NNCT-tests after an overall test yields significant interaction. We also introduce the concepts of total, strong, and partial segregation/association to label levels of these patterns. We compare these new tests with the existing NNCT-tests in literature with simulations as well and illustrate the NNCT-tests on an ecological data set.
Keeping greed good: sparse regression under design uncertainty with application to biomass characterization

In this paper, we consider the classic measurement error regression scenario in which our independent, or design, variables are observed with several sources of additive noise. We will show that our motivating example's replicated measurements on both the design and dependent variables may be leveraged to enhance a sparse regression algorithm. Specifically, we estimate the variance and use it to scale our design variables. We demonstrate the efficacy of scaling from several points of view and validate it empirically with a biomass characterization data set using two of the most widely used sparse algorithms: least angle regression (LARS) and the Dantzig selector (DS).
Revisiting Marginal Regression

The lasso has become an important practical tool for high dimensional regression as well as the object of intense theoretical investigation. But despite the availability of efficient algorithms, the lasso remains computationally demanding in regression problems where the number of variables vastly exceeds the number of data points. A much older method, marginal regression, largely displaced by the lasso, offers a promising alternative in this case. Computation for marginal regression is practical even when the dimension is very high. In this paper, we study the relative performance of the lasso and marginal regression for regression problems in three different regimes: (a) exact reconstruction in the noise-free and noisy cases when design and coefficients are fixed, (b) exact reconstruction in the noise-free case when the design is fixed but the coefficients are random, and (c) reconstruction in the noisy case where performance is measured by the number of coefficients whose sign is incorrect. In the first regime, we compare the conditions for exact reconstruction of the two procedures, find examples where each procedure succeeds while the other fails, and characterize the advantages and disadvantages of each. In the second regime, we derive conditions under which marginal regression will provide exact reconstruction with high probability. And in the third regime, we derive rates of convergence for the procedures and offer a new partitioning of the ``phase diagram,'' that shows when exact or Hamming reconstruction is effective.
Change point analysis of an exponential model based on Phi-divergence test-statistics: simulated critical points case

Recently Batsidis \textit{et al.} (2011) have presented a new procedure based on divergence measures for testing the hypothesis of the existence of a change point in exponential populations. A simulation study was carried out, in this paper, using the asymptotic critical points obtained from the asymptotic distribution of the new test statistics introduced there. The main purpose of this paper is to study the behavior of the test statistics introduced in the cited paper of Batsidis \textit{et al.} (2011), using simulated critical points.
On the penultimate tail behavior of Weibull-type models

The Gumbel max-domain of attraction corresponds to a null tail index which do not distinguish the different tail weights that might exist between distributions within this class. The Weibull-type distributions form an important subgroup of this latter and includes the so-called \emph{Weibull-tail coefficient}, usually denoted \theta, that specifies the tail behavior, with larger values indicating slower tail decay. Here we shall see that the Weibull-type distributions present a penultimate tail behavior Fr\'echet if \theta>1 and a penultimate tail behavior Weibull whenever \theta<1.
A Bias-Reduced Estimator for the Mean of a Heavy-Tailed Distribution with an Infinite Second Moment

We use bias-reduced estimators of high quantiles, of heavy-tailed distributions, to introduce a new estimator of the mean in the case of infinite second moment. The asymptotic normality of the proposed estimator is established and checked, in a simulation study, by four of the most popular goodness-of-fit tests for different sample sizes. Moreover, we compare, in terms of bias and mean squared error, our estimator with Peng's estimator (Peng, 2001) and we evaluate the accuracy of some resulting confidence intervals.