4期

最新文章

• STATISTICAL INFERENCE ON A CHANGING EXTREME VALUE DEPENDENCE STRUCTURE AI翻译
  作者：Drees, Holger
  作者单位：University of Hamburg
  摘要：We analyze the extreme value dependence of independent, not necesMore specifically, we propose estimators of the spectral measure locally at some time point and of the spectral measures integrated over time. The uniform asymptotic normality of these estimators is proved under suitable nonparametric smoothness and regularity assumptions. We then use the process convergence of the integrated spectral measure to devise consistent tests for the null hypothesis that the spectral measure does not ch...
• NOISY LINEAR INVERSE PROBLEMS UNDER CONVEX CONSTRAINTS: EXACT RISK ASYMPTOTICS IN HIGH DIMENSIONS AI翻译
  作者：Han, Qiyang
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：In the standard Gaussian linear measurement model Y = X mu 0 + xi is an element of Rm with a fixed noise level sigma > 0, we consider the problem of estimating the unknown signal mu 0 under a convex constraint mu 0 is an element of K , where K is a closed convex set in Rn. We show that the risk of the natural convex constrained least squares estimator (LSE) mu?(sigma) can be characterized exactly in high dimensional limits, by that of the convex constrained LSE mu ?seQ K in the corresponding G...
• POST-SELECTION INFERENCE VIA ALGORITHMIC STABILITY AI翻译
  作者：Zrnic, Tijana; Jordan, Michael I.
  作者单位：University of California System; University of California Berkeley
  摘要：When the target of statistical inference is chosen in a data-driven manner, the guarantees provided by classical theories vanish. We propose a solution to the problem of inference after selection by building on the framework of algorithmic stability, in particular its branch with origins in the field of differential privacy. Stability is achieved via randomization of selection and it serves as a quantitative measure that is sufficient to obtain nontrivial post-selection corrections for classic...
• OPTIMAL CHANGE-POINT DETECTION AND LOCALIZATION AI翻译
  作者：Verzelen, Nicolas; Fromont, Magalie; Lerasle, Matthieu; Reynaud-Bouret, Patricia
  作者单位：INRAE; Universite de Rennes; Institut Polytechnique de Paris; ENSAE Paris; Universite Cote d'Azur
  摘要：Given a times series Y in Rn, with a piecewise constant mean and independent components, the twin problems of change-point detection and change-point localization, respectively amount to detecting the existence of times where the mean varies and estimating the positions of those changepoints. In this work, we tightly characterize optimal rates for both problems and uncover the phase transition phenomenon from a global testing problem to a local estimation problem. Introducing a suitable defini...
• ORDER-OF-ADDITION ORTHOGONAL ARRAYS TO STUDY THE EFFECT OF TREATMENT ORDERING AI翻译
  作者：Schoen, Eric D.; Mee, Robert W.
  作者单位：KU Leuven; University of Tennessee System; University of Tennessee Knoxville
  摘要：The effect of the order in which a set of m treatments is applied can be modeled by relative-position factors that indicate whether treatment i is carried out before or after treatment j, or by the absolute position for treatment i in the sequence. A design with the same normalized information matrix as the design with all m! sequences is D- and G-optimal for the main-effects model involving the relative-position factors. We prove that such designs are also I-optimal for this model and D-optim...
• SINGLE INDEX FRECHET REGRESSION AI翻译
  作者：Bhattacharjee, Satarupa; Muller, Hans-georg
  作者单位：University of California System; University of California Davis
  摘要：Single index models provide an effective dimension reduction tool in regression, especially for high-dimensional data, by projecting a general multivariate predictor onto a direction vector. We propose a novel single-index model for regression models where metric space-valued random object responses are coupled with multivariate Euclidean predictors. The responses in this regression model include complex, non-Euclidean data, including covariance matrices, graph Laplacians of networks and univa...
• PROJECTED STATE-ACTION BALANCING WEIGHTS FOR OFFLINE REINFORCEMENT LEARNING AI翻译
  作者：Wang, Jiayi; Qi, Zhengling; Wong, Raymond K. W.
  作者单位：University of Texas System; University of Texas Dallas; George Washington University; Texas A&M University System; Texas A&M University College Station
  摘要：Off-policy evaluation is considered a fundamental and challenging problem in reinforcement learning (RL). This paper focuses on value estimation of a target policy based on pre-collected data generated from a possibly different policy, under the framework of infinite-horizon Markov decision processes. Motivated by the recently developed marginal importance sampling method in RL and the covariate balancing idea in causal inference, we propose a novel estimator with approximately projected state...
• BRIDGING FACTOR AND SPARSE MODELS AI翻译
  作者：Fan, Jianqing; Masini, Ricardo P.; Medeiros, Marcelo C.
  作者单位：Princeton University; University of California System; University of California Davis; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Factor and sparse models are widely used to impose a low-dimensional structure in high-dimensions. However, they are seemingly mutually exclusive. We propose a lifting method that combines the merits of these two models in a supervised learning methodology that allows for efficiently exploring all the information in high-dimensional datasets. The method is based on a flexible model for high-dimensional panel data with observable and/or latent common factors and idiosyncratic components. The mo...
• GRAPHICAL MODELS FOR NONSTATIONARY TIME SERIES AI翻译
  作者：Basu, Sumanta; Rao, Suhasini Subba
  作者单位：Cornell University; Texas A&M University System; Texas A&M University College Station
  摘要：We propose NonStGM, a general nonparametric graphical modeling framework, for studying dynamic associations among the components of a nonstationary multivariate time series. It builds on the framework of Gaussian graphical models (GGM) and stationary time series graphical models (StGM) and complements existing works on parametric graphical models based on change point vector autoregressions (VAR). Analogous to StGM, the proposed framework captures conditional noncorrelations (both intertempora...
• MATCHING RECOVERY THRESHOLD FOR CORRELATED RANDOM GRAPHS AI翻译
  作者：Ding, Jian; Du, Hang
  作者单位：Peking University
  摘要：For two correlated graphs which are independently sub-sampled from a common Erdos-Renyi graph G(n, p) , we wish to recover their latent vertex matching from the observation of these two graphs without labels. When p = n-alpha +o(1) for alpha E (0 , 1] , we establish a sharp information-theoretic threshold for whether it is possible to correctly match a positive fraction of vertices. Our result sharpens a constant factor in a recent work by Wu, Xu and Yu.