The Annals of Statistics

最新文章

• A POWER ANALYSIS FOR MODEL-X KNOCKOFFS WITH fp-REGULARIZED STATISTICS AI翻译
  作者：Weinstein, Asaf; Su, Weijie J.; Bogdan, Malgorzata; Barber, Rina Foygel; Candes, Emmanuel J.
  作者单位：Hebrew University of Jerusalem; University of Pennsylvania; University of Wroclaw; University of Chicago; Stanford University; Stanford University
  摘要：Variable selection properties of procedures utilizing penalized-likelihood estimates is a central topic in the study of high-dimensional linear regression problems. Existing literature emphasizes the quality of ranking of the variables by such procedures as reflected in the receiver operating characteristic curve or in prediction performance. Specifically, recent works have harnessed modern theory of approximate message-passing (AMP) to obtain, in a particular setting, exact asymptotic predict...
• LEARNING SPARSE GRAPHONS AND THE GENERALIZED KESTEN-STIGUM THRESHOLD AI翻译
  作者：Abbe, Emmanuel; Li, Shuangping; Sly, Allan
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Princeton University; Princeton University
  摘要：The problem of learning graphons has attracted considerable attention across several scientific communities, with significant progress over the re-cent years in sparser regimes. Yet, the current techniques still require diverg-ing degrees in order to succeed with efficient algorithms in the challenging cases where the local structure of the graph is homogeneous. This paper pro-vides an efficient algorithm to learn graphons in the constant expected degree regime. The algorithm is shown to succe...
• OPTIMAL ESTIMATION AND COMPUTATIONAL LIMIT OF LOW-RANK GAUSSIAN MIXTURES AI翻译
  作者：Lyu, Zhongyuan; Xia, Dong
  作者单位：Hong Kong University of Science & Technology
  摘要：Structural matrix-variate observations routinely arise in diverse fields such as multilayer network analysis and brain image clustering. While data of this type have been extensively investigated with fruitful outcomes being delivered, the fundamental questions like its statistical optimality and computational limit are largely under-explored. In this paper, we propose a low-rank Gaussian mixture model (LrMM) assuming each matrix-valued observation has a planted low-rank structure. Minimax low...
• RATE-OPTIMAL ROBUST ESTIMATION OF HIGH-DIMENSIONAL VECTOR AUTOREGRESSIVE MODELS AI翻译
  作者：Wang, Di; Tsay, Ruey S.
  作者单位：Shanghai Jiao Tong University; University of Chicago
  摘要：High-dimensional time series data appear in many scientific areas in the current data-rich environment. Analysis of such data poses new challenges to data analysts because of not only the complicated dynamic dependence between the series, but also the existence of aberrant observations, such as missing values, contaminated observations, and heavy-tailed distributions. For high-dimensional vector autoregressive (VAR) models, we introduce a unified estimation procedure that is robust to model mi...
• ROBUST HIGH-DIMENSIONAL TUNING FREE MULTIPLE TESTING AI翻译
  作者：Fan, Jianqing; Lou, Zhipeng; Yu, Mengxin
  作者单位：Princeton University; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University of Pennsylvania
  摘要：A stylized feature of high-dimensional data is that many variables have heavy tails, and robust statistical inference is critical for valid large-scale statistical inference. Yet, the existing developments such as Winsorization, Huberization and median of means require the bounded second moments and involve variable-dependent tuning parameters, which hamper their fidelity in applications to large-scale problems. To liberate these constraints, this paper revisits the celebrated Hodges-Lehmann (...
• A GENERAL CHARACTERIZATION OF OPTIMAL TIE-BREAKER DESIGNS AI翻译
  作者：Li, Harrison H.; Owen, Art B.
  作者单位：Stanford University
  摘要：Tie-breaker designs trade off a measure of statistical efficiency against a with higher values of a running variable x. The efficiency measure can be any continuous function of the expected information matrix in a two-line regression model. The short-term gain is expressed as the covariance between the running variable and the treatment indicator. We investigate how to choose design functions p(x) specifying the probability of treating a subject with running variable x in order to optimize the...
• ASYMPTOTIC NORMALITY FOR EIGENVALUE STATISTICS OF A GENERAL SAMPLE COVARIANCE MATRIX WHEN p/n? 8 AND APPLICATIONS AI翻译
  作者：Qiu, Jiaxin; Li, Zeng; Yao, Jianfeng
  作者单位：University of Hong Kong; Southern University of Science & Technology; The Chinese University of Hong Kong, Shenzhen
  摘要：The asymptotic normality for a large family of eigenvalue statistics of a general sample covariance matrix is derived under the ultrahigh-dimensional setting, that is, when the dimension to sample size ratio p/n & RARR; & INFIN;. Based on this CLT result, we extend the covariance matrix test problem to the new ultra-high-dimensional context, and apply it to test a matrix-valued white noise. Simulation experiments are conducted for the investigation of finite-sample properties of the general as...
• INFERENCE IN ISING MODELS ON DENSE REGULAR GRAPHS AI翻译
  作者：Xu, Yuanzhe; Mukherjee, Sumit
  作者单位：Columbia University
  摘要：In this paper, we derive the limit of experiments for one-parameter Ising models on dense regular graphs. In particular, we show that the limiting ex-periment is Gaussian in the low temperature regime, and non-Gaussian in the critical regime. We also derive the limiting distributions of the maxi-mum likelihood and maximum pseudolikelihood estimators, and study limit-ing power for tests of hypothesis against contiguous alternatives. To the best of our knowledge, this is the first attempt at est...
• GROUPED VARIABLE SELECTION WITH DISCRETE OPTIMIZATION: COMPUTATIONAL AND STATISTICAL PERSPECTIVES AI翻译
  作者：Hazimeh, Hussein; Mazumder, Rahul; Radchenko, Peter
  作者单位：Alphabet Inc.; Google Incorporated; Massachusetts Institute of Technology (MIT); University of Sydney
  摘要：We present a new algorithmic framework for grouped variable selection that is based on discrete mathematical optimization. While there exist several appealing approaches based on convex relaxations and nonconvex heuristics, we focus on optimal solutions for the l(0)-regularized formulation, a problem that is relatively unexplored due to computational challenges. Our methodol-ogy covers both high-dimensional linear regression and nonparametric sparse additive modeling with smooth components. Ou...
• COMPARISON OF MARKOV CHAINS VIA WEAK POINCAR? INEQUALITIES WITH APPLICATION TO PSEUDO-MARGINAL MCMC AI翻译
  作者：Andrieu, Christophe; Lee, Anthony; Power, Sam; Wang, Andi Q.
  作者单位：University of Bristol
  摘要：We investigate the use of a certain class of functional inequalities known as weak Poincare inequalities to bound convergence of Markov chains to equilibrium. We show that this enables the straightforward and transpar-ent derivation of subgeometric convergence bounds for methods such as the Independent Metropolis-Hastings sampler and pseudo-marginal methods for intractable likelihoods, the latter being subgeometric in many practical settings. These results rely on novel quantitative comparison...