2020

最新文章

• MINIMAX ESTIMATION OF LARGE PRECISION MATRICES WITH BANDABLE CHOLESKY FACTOR AI翻译
  作者：Liu, Yu; Ren, Zhao
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
  摘要：The last decade has witnessed significant methodological and theoretical advances in estimating large precision matrices. In particular, there are scientific applications such as longitudinal data, meteorology and spectroscopy in which the ordering of the variables can be interpreted through a bandable structure on the Cholesky factor of the precision matrix. However, the minimax theory has still been largely unknown, as opposed to the well established minimax results over the corresponding ba...
• POST HOC CONFIDENCE BOUNDS ON FALSE POSITIVES USING REFERENCE FAMILIES AI翻译
  作者：Blanchard, Gilles; Neuvial, Pierre; Roquain, Etienne
  作者单位：University of Potsdam; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Universite Federale Toulouse Midi-Pyrenees (ComUE); Universite de Toulouse; Institut National des Sciences Appliquees de Toulouse; Universite Toulouse III - Paul Sabatier; Sorbonne Universite; Centre National de la Recherche Scientifique (CNRS); Universite Paris Cite
  摘要：We follow a post hoc, user-agnostic approach to false discovery control in a large-scale multiple testing framework, as introduced by Genovese and Wasserman [J. Amer. Statist. Assoc. 101 (2006) 1408-1417], Goeman and Solari [Statist. Sci. 26 (2011) 584-597]: the statistical guarantee on the number of correct rejections must hold for any set of candidate items, possibly selected by the user after having seen the data. To this end, we introduce a novel point of view based on a family of referenc...
• D-OPTIMAL DESIGNS FOR MULTINOMIAL LOGISTIC MODELS AI翻译
  作者：Bu, Xianwei; Majumdar, Dibyen; Yang, Jie
  作者单位：AbbVie; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
  摘要：We consider optimal designs for general multinomial logistic models, which cover baseline-category, cumulative, adjacent-categories and continuation-ratio logit models, with proportional odds, nonproportional odds or partial proportional odds assumption. We derive the corresponding Fisher information matrices in three different forms to facilitate their calculations, determine the conditions for their positive definiteness, and search for optimal designs. We conclude that, unlike the designs f...
• MODEL-ASSISTED INFERENCE FOR TREATMENT EFFECTS USING REGULARIZED CALIBRATED ESTIMATION WITH HIGH-DIMENSIONAL DATA AI翻译
  作者：Tan, Zhiqiang
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：Consider the problem of estimating average treatment effects when a large number of covariates are used to adjust for possible confounding through outcome regression and propensity score models. We develop new methods and theory to obtain not only doubly robust point estimators for average treatment effects, which remain consistent if either the propensity score model or the outcome regression model is correctly specified, but also model-assisted confidence intervals, which are valid when the ...
• CLUSTERING IN BLOCK MARKOV CHAINS AI翻译
  作者：Sanders, Jaron; Proutiere, Alexandre; Yun, Se-Young
  作者单位：Royal Institute of Technology; Delft University of Technology; Korea Advanced Institute of Science & Technology (KAIST)
  摘要：This paper considers cluster detection in Block Markov Chains (BMCs). These Markov chains are characterized by a block structure in their transition matrix. More precisely, the n possible states are divided into a finite number of K groups or clusters, such that states in the same cluster exhibit the same transition rates to other states. One observes a trajectory of the Markov chain, and the objective is to recover, from this observation only, the (initially unknown) clusters. In this paper, ...
• VARIATIONAL ANALYSIS OF CONSTRAINED M-ESTIMATORS AI翻译
  作者：Royset, Johannes O.; Wets, Roger J-B
  作者单位：United States Department of Defense; United States Navy; Naval Postgraduate School; University of California System; University of California Davis
  摘要：We propose a unified framework for establishing existence of nonparametric M-estimators, computing the corresponding estimates, and proving their strong consistency when the class of functions is exceptionally rich. In particular, the framework addresses situations where the class of functions is complex involving information and assumptions about shape, pointwise bounds, location of modes, height at modes, location of level-sets, values of moments, size of subgradients, continuity, distance t...
• ESTIMATION AND INFERENCE FOR PRECISION MATRICES OF NONSTATIONARY TIME SERIES AI翻译
  作者：Ding, Xiucai; Zhou, Zhou
  作者单位：University of Toronto
  摘要：We consider the estimation of and inference on precision matrices of a rich class of univariate locally stationary linear and nonlinear time series, assuming that only one realization of the time series is observed. Using a Cholesky decomposition technique, we show that the precision matrices can be directly estimated via a series of least squares linear regressions with smoothly time-varying coefficients. The method of sieves is utilized for the estimation and is shown to be optimally adaptiv...
• ROBUST INFERENCE VIA MULTIPLIER BOOTSTRAP AI翻译
  作者：Chen, Xi; Zhou, Wen-Xin
  作者单位：New York University; University of California System; University of California San Diego
  摘要：This paper investigates the theoretical underpinnings of two fundamental statistical inference problems, the construction of confidence sets and large-scale simultaneous hypothesis testing, in the presence of heavy-tailed data. With heavy-tailed observation noise, finite sample properties of the least squares-based methods, typified by the sample mean, are suboptimal both theoretically and empirically. In this paper, we demonstrate that the adaptive Huber regression, integrated with the multip...
• WORST-CASE VERSUS AVERAGE-CASE DESIGN FOR ESTIMATION FROM PARTIAL PAIRWISE COMPARISONS AI翻译
  作者：Pananjady, Ashwin; Mao, Cheng; Muthukumar, Vidya; Wainwright, Martin J.; Courtade, Thomas A.
  作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley; Yale University
  摘要：Pairwise comparison data arises in many domains, including tournament rankings, web search and preference elicitation. Given noisy comparisons of a fixed subset of pairs of items, we study the problem of estimating the underlying comparison probabilities under the assumption of strong stochastic transitivity (SST). We also consider the noisy sorting subclass of the SST model. We show that when the assignment of items to the topology is arbitrary, these permutation-based models, unlike their pa...
• PREDICTION ERROR AFTER MODEL SEARCH AI翻译
  作者：Tian, Xiaoying
  作者单位：Stanford University
  摘要：Estimation of the prediction error of a linear estimation rule is difficult if the data analyst also uses data to select a set of variables and constructs the estimation rule using only the selected variables. In this work, we propose an asymptotically unbiased estimator for the prediction error after model search. Under some additional mild assumptions, we show that our estimator converges to the true prediction error in L-2 at the rate of O(n(-1/2)), with n being the number of data points. O...