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作者：Chang, Jinyuan; Chen, Song Xi; Tang, Cheng Yong; Wu, Tong Tong
作者单位：Southwestern University of Finance & Economics - China; Peking University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; University of Rochester
摘要：High-dimensional statistical inference with general estimating equations is challenging and remains little explored. We study two problems in the area: confidence set estimation for multiple components of the model parameters, and model specifications tests. First, we propose to construct a new set of estimating equations such that the impact from estimating the high-dimensional nuisance parameters becomes asymptotically negligible. The new construction enables us to estimate a valid confidenc...
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作者：Schmon, S. M.; Deligiannidis, G.; Doucet, A.; Pitt, M. K.
作者单位：University of Oxford; University of London; King's College London
摘要：The pseudo-marginal algorithm is a variant of the Metropolis-Hastings algorithm which samples asymptotically from a probability distribution when it is only possible to estimate unbiasedly an unnormalized version of its density. Practically, one has to trade off the computational resources used to obtain this estimator against the asymptotic variances of the ergodic averages obtained by the pseudo-marginal algorithm. Recent works on optimizing this trade-off rely on some strong assumptions, wh...
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作者：Ma, Rong; Cai, T. Tony; Li, Hongzhe
作者单位：University of Pennsylvania; University of Pennsylvania
摘要：Motivated by the problem of estimating bacterial growth rates for genome assemblies from shotgun metagenomic data, we consider the permuted monotone matrix model Y = Theta Pi + Z where Y is an element of R-nxp is observed, Theta is an element of R-nxp an unknown approximately rank-one signal matrix with monotone rows, Pi is an element of R-pxp is an unknown permutation matrix, and Z is an element of R-nxp is the noise matrix. In this article we study estimation of the extreme values associated...
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作者：Amico, M.; Van Keilegom, I; Han, B.
作者单位：KU Leuven
摘要：Survival analysis relies on the hypothesis that, if the follow-up is long enough, the event of interest will eventually be observed for all observations. This assumption, however, is often not realistic. The survival data then contain a cure fraction. A common approach to modelling and analysing this type of data consists in using cure models. Two types of information can therefore be obtained: the survival at a given time and the cure status, both possibly modelled as a function of the covari...
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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 ...
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作者：Wang, Linbo; Zhang, Yuexia; Richardson, Thomas S.; Robins, James M.
作者单位：University of Toronto; University of Toronto; University of Washington; University of Washington Seattle; Harvard University; Harvard T.H. Chan School of Public Health
摘要：Instrumental variables are widely used to deal with unmeasured confounding in observational studies and imperfect randomized controlled trials. In these studies, researchers often target the so-called local average treatment effect as it is identifiable under mild conditions. In this paper we consider estimation of the local average treatment effect under the binary instrumental variable model. We discuss the challenges of causal estimation with a binary outcome and show that, surprisingly, it...
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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...
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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...
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作者：Ahfock, D. C.; Astle, W. J.; Richardson, S.
作者单位：University of Cambridge; MRC Biostatistics Unit
摘要：Sketching is a probabilistic data compression technique that has been largely developed by the computer science community. Numerical operations on big datasets can be intolerably slow; sketching algorithms address this issue by generating a smaller surrogate dataset. Typically, inference proceeds on the compressed dataset. Sketching algorithms generally use random projections to compress the original dataset, and this stochastic generation process makes them amenable to statistical analysis. W...
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作者：Lei, Lihua; Ding, Peng
作者单位：Stanford University; University of California System; University of California Berkeley
摘要：Randomized experiments have become important tools in empirical research. In a completely randomized treatment-control experiment, the simple difference in means of the outcome is unbiased for the average treatment effect, and covariate adjustment can further improve the efficiency without assuming a correctly specified outcome model. In modern applications, experimenters often have access to many covariates, motivating the need for a theory of covariate adjustment under the asymptotic regime ...
