-
作者：Bogomolov, Marina; Peterson, Christine B.; Benjamini, Yoav; Sabatti, Chiara
作者单位：Technion Israel Institute of Technology; University of Texas System; UTMD Anderson Cancer Center; Tel Aviv University; Stanford University
摘要：We introduce a multiple testing procedure that controls global error rates at multiple levels of resolution. Conceptually, we frame this problem as the selection of hypotheses that are organized hierarchically in a tree structure. We describe a fast algorithm and prove that it controls relevant error rates given certain assumptions on the dependence between the p-values. Through simulations, we demonstrate that the proposed procedure provides the desired guarantees under a range of dependency ...
-
作者：Zhou, Wenzhuo; Zhu, Ruoqing; Zeng, Donglin
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of North Carolina; University of North Carolina Chapel Hill
摘要：Learning an individualized dose rule in personalized medicine is a challenging statistical problem. Existing methods often suffer from the curse of dimensionality, especially when the decision function is estimated nonparametrically. To tackle this problem, we propose a dimension reduction framework that effectively reduces the estimation to an optimization on a lower-dimensional subspace of the covariates. We exploit the fact that the individualized dose rule can be defined in a subspace span...
-
作者：He, Yinqiu; Meng, Bo; Zeng, Zhenghao; Xu, Gongjun
作者单位：University of Michigan System; University of Michigan; Chinese Academy of Sciences; University of Science & Technology of China, CAS
摘要：Wilks' theorem, which offers universal chi-squared approximations for likelihood ratio tests, is widely used in many scientific hypothesis testing problems. For modern datasets with increasing dimension, researchers have found that the conventional Wilks' phenomenon of the likelihood ratio test statistic often fails. Although new approximations have been proposed in high-dimensional settings, there still lacks a clear statistical guideline regarding how to choose between the conventional and n...
-
作者：Lin, Zhenhua; Wang, Jane-Ling; Zhong, Qixian
作者单位：National University of Singapore; University of California System; University of California Davis; Tsinghua University
摘要：Estimation of mean and covariance functions is fundamental for functional data analysis. While this topic has been studied extensively in the literature, a key assumption is that there are enough data in the domain of interest to estimate both the mean and covariance functions. We investigate mean and covariance estimation for functional snippets in which observations from a subject are available only in an interval of length strictly, and often much, shorter than the length of the whole inter...
-
作者：Matsushita, Yukitoshi; Otsu, Taisuke
作者单位：Hitotsubashi University; University of London; London School Economics & Political Science
摘要：This article aims to shed light on inference problems for statistical models under alternative or nonstandard asymptotic frameworks from the perspective of the jackknife empirical likelihood. Examples include small-bandwidth asymptotics for semiparametric inference and goodness-of-fit testing, sparse-network asymptotics, many-covariates asymptotics for regression models, and many-weak-instruments asymptotics for instrumental variable regression. We first establish Wilks' theorem for the jackkn...
-
作者：Clarte, Gregoire; Robert, Christian P.; Ryder, Robin J.; Stoehr, Julien
作者单位：Universite PSL; Universite Paris-Dauphine
摘要：Approximate Bayesian computation methods are useful for generative models with intractable likelihoods. These methods are, however, sensitive to the dimension of the parameter space, requiring exponentially increasing resources as this dimension grows. To tackle this difficulty we explore a Gibbs version of the approximate Bayesian computation approach that runs component-wise approximate Bayesian computation steps aimed at the corresponding conditional posterior distributions, and based on su...
-
作者：Qian, Tianchen; Yoo, Hyesun; Klasnja, Predrag; Almirall, Daniel; Murphy, Susan A.
作者单位：University of California System; University of California Irvine; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; Harvard University
摘要：Advances in digital technology and wearables have made it possible to deliver behavioural mobile health interventions to individuals in their everyday lives. Micro-randomized trials are increasingly used to provide data to inform the construction of these interventions. In a micro-randomized trial, each individual is repeatedly randomized among multiple intervention options, often hundreds or even thousands of times over the course of the trial. The work reported in this article is motivated b...
-
作者：Zhang, Y.; Laber, E. B.
作者单位：University of Rhode Island; Duke University
