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作者：Wong, Kin Yau; Zeng, Donglin; Lin, D. Y.
作者单位：Hong Kong Polytechnic University; University of North Carolina; University of North Carolina Chapel Hill
摘要：In long-term follow-up studies, data are often collected on repeated measures of multivariate response variables as well as on time to the occurrence of a certain event. To jointly analyze such longitudinal data and survival time, we propose a general class of semiparametric latent-class models that accommodates a heterogeneous study population with flexible dependence structures between the longitudinal and survival outcomes. We combine nonparametric maximum likelihood estimation with sieve e...
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作者：Barber, Rina Foygel; Janson, Lucas
作者单位：University of Chicago; Harvard University
摘要：Goodness-of-fit (GoF) testing is ubiquitous in statistics, with direct ties to model selection, confidence interval construction, conditional independence testing, and multiple testing, just to name a few applications. While testing the GoF of a simple (point) null hypothesis provides an analyst great flexibility in the choice of test statistic while still ensuring validity, most GoF tests for composite null hypotheses are far more constrained, as the test statistic must have a tractable distr...
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作者：Schramm, Tselil; Wein, Alexander S.
作者单位：Stanford University; New York University
摘要：One fundamental goal of high-dimensional statistics is to detect or recover planted structure (such as a low-rank matrix) hidden in noisy data. A growing body of work studies low-degree polynomials as a restricted model of computation for such problems: it has been demonstrated in various settings that low-degree polynomials of the data can match the statistical performance of the best known polynomial-time algorithms. Prior work has studied the power of low-degree polynomials for the task of ...
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作者：Bradic, Jelena; Fan, Jianqing; Zhu, Yinchu
作者单位：University of California System; University of California San Diego; University of California System; University of California San Diego; Princeton University; Brandeis University; Brandeis University
摘要：Understanding statistical inference under possibly nonsparse high-dimensional models has gained much interest recently. For a given component of the regression coefficient, we show that the difficulty of the problem depends on the sparsity of the corresponding row of the precision matrix of the covariates, not the sparsity of the regression coefficients. We develop new concepts of uniform and essentially uniform nontestability that allow the study of limitations of tests across a broad set of ...
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作者：Vovk, Vladimir; Wang, Bin; Wang, Ruodu
作者单位：University of London; Royal Holloway University London; Chinese Academy of Sciences; University of Waterloo
摘要：Methods of merging several p-values into a single p-value are important in their own right and widely used in multiple hypothesis testing. This paper is the first to systematically study the admissibility (in Wald's sense) of p-merging functions and their domination structure, without any information on the dependence structure of the input p-values. As a technical tool, we use the notion of e-values, which are alternatives to p-values recently promoted by several authors. We obtain several re...
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作者：Fithian, William; Lei, Lihua
作者单位：University of California System; University of California Berkeley; Stanford University
摘要：We introduce a new class of methods for finite-sample false discovery rate (FDR) control in multiple testing problems with dependent test statistics where the dependence is known. Our approach separately calibrates a data -dependent p-value rejection threshold for each hypothesis, relaxing or tight-ening the threshold as appropriate to target exact FDR control. In addition to our general framework, we propose a concrete algorithm, the dependence-adjusted Benjamini-Hochberg (dBH) procedure, whi...
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作者：Zhu, Banghua; Jiao, Jiantao; Steinhardt, Jacob
作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley
摘要：Robust statistics traditionally focuses on outliers, or perturbations in total variation distance. However, a dataset could be maliciously corrupted in many other ways, such as systematic measurement errors and missing covariates. We consider corruption in either TV or Wasserstein distance, and show that robust estimation is possible whenever the true population distribution satisfies a property called generalized resilience, which holds under moment or hypercontractive conditions. For TV corr...
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作者：Chen, Pinhan; Gao, Chao; Zhang, Anderson Y.
作者单位：University of Chicago; University of Pennsylvania
摘要：We consider the problem of ranking n players from partial pairwise comparison data under the Bradley-Terry-Luce model. For the first time in the literature, the minimax rate of this ranking problem is derived with respect to the Kendall's tau distance that measures the difference between two rank vectors by counting the number of inversions. The minimax rate of ranking exhibits a transition between an exponential rate and a polynomial rate depending on the magnitude of the signal-to-noise rati...
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作者：Leung, Michael P.
作者单位：University of California System; University of California Santa Cruz
摘要：We consider a potential outcomes model in which interference may be present between any two units but the extent of interference diminishes with spatial distance. The causal estimand is the global average treatment effect, which compares outcomes under the counterfactuals that all or no units are treated. We study a class of designs in which space is partitioned into clusters that are randomized into treatment and control. For each design, we estimate the treatment effect using a Horvitz-Thomp...
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作者：Sourisseau, Matt; Wu, Hau-Tieng; Zhou, Zhou
作者单位：University of Toronto; Duke University; Duke University; University of Toronto
摘要：We provide a statistical analysis of a tool in nonlinear-type time-frequency analysis, the synchrosqueezing transform (SST), for both the null and nonnull cases. The intricate nonlinear interaction of different quantities in SST is quantified by carefully analyzing relevant multivariate complex Gaussian random variables. Specifically, we provide the quotient distribution of dependent and improper complex Gaussian random variables. Then a central limit theorem result for SST is established. As ...
