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作者：Prenen, Leen; Braekers, Roel; Duchateau, Luc
作者单位：Hasselt University; Ghent University
摘要：For the analysis of clustered survival data, two different types of model that take the association into account are commonly used: frailty models and copula models. Frailty models assume that, conditionally on a frailty term for each cluster, the hazard functions of individuals within that cluster are independent. These unknown frailty terms with their imposed distribution are used to express the association between the different individuals in a cluster. Copula models in contrast assume that...
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作者：Cai, T. Tony; Sun, Wenguang
作者单位：University of Pennsylvania; University of Southern California
摘要：A common feature in large-scale scientific studies is that signals are sparse and it is desirable to narrow down significantly the focus to a much smaller subset in a sequential manner. We consider two related data screening problems: one is to find the smallest subset such that it virtually contains all signals and another is to find the largest subset such that it essentially contains only signals. These screening problems are closely connected to but distinct from the more conventional sign...
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作者：Care, Algo; Garatti, Simone; Campi, Marco C.
作者单位：HUN-REN; HUN-REN Institute for Computer Science & Control; Polytechnic University of Milan; University of Brescia
摘要：A sensible use of an estimation method requires that assessment criteria for the quality of the estimate be available. We present a coverage theory for the least squares estimate. By suitably modifying the empirical costs, one constructs statistics that are guaranteed to cover with known probability the cost associated with a next, still unseen, member of the population. All results of this paper are distribution free and can be applied to least squares problems in use across a variety of fiel...
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作者：Roy, Sandipan; Atchade, Yves; Michailidis, George
作者单位：University of London; University College London; University of Michigan System; University of Michigan; State University System of Florida; University of Florida
摘要：The paper investigates a change point estimation problem in the context of high dimensional Markov random-field models. Change points represent a key feature in many dynamically evolving network structures. The change point estimate is obtained by maximizing a profile penalized pseudolikelihood function under a sparsity assumption. We also derive a tight bound for the estimate, up to a logarithmic factor, even in settings where the number of possible edges in the network far exceeds the sample...
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作者：Oates, Chris J.; Girolami, Mark; Chopin, Nicolas
作者单位：University of Technology Sydney; University of Warwick; Alan Turing Institute; Institut Polytechnique de Paris; ENSAE Paris; Institut Polytechnique de Paris; ENSAE Paris
摘要：A non-parametric extension of control variates is presented. These leverage gradient information on the sampling density to achieve substantial variance reduction. It is not required that the sampling density be normalized. The novel contribution of this work is based on two important insights: a trade-off between random sampling and deterministic approximation and a new gradient-based function space derived from Stein's identity. Unlike classical control variates, our estimators improve rates...
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作者：Chick, Stephen; Forster, Martin; Pertile, Paolo
作者单位：INSEAD Business School; University of York - UK; University of Verona
摘要：We propose a Bayesian decision theoretic model of a fully sequential experiment in which the real-valued primary end point is observed with delay. The goal is to identify the sequential experiment which maximizes the expected benefits of technology adoption decisions, minus sampling costs. The solution yields a unified policy defining the optimal do not experiment'-fixed sample size experiment'-sequential experiment' regions and optimal stopping boundaries for sequential sampling, as a functio...
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作者：Leeb, Hannes; Kabaila, Paul
作者单位：University of Vienna; La Trobe University
摘要：In the Gaussian linear regression model (with unknown mean and variance), we show that the standard confidence set for one or two regression coefficients is admissible in the sense of Joshi. This solves a long-standing open problem in mathematical statistics, and this has important implications on the performance of modern inference procedures post model selection or post shrinkage, particularly in situations where the number of parameters is larger than the sample size. As a technical contrib...
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作者：Wang, Linbo; Richardson, Thomas S.; Zhou, Xiao-Hua
作者单位：University of Washington; University of Washington Seattle; US Department of Veterans Affairs; Veterans Health Administration (VHA); Vet Affairs Puget Sound Health Care System
摘要：It is common that, in multiarm randomized trials, the outcome of interest is truncated by death', meaning that it is only observed or well-defined conditioning on an intermediate outcome. In this case, in addition to pairwise contrasts, the joint inference for all treatment arms is also of interest. Under a monotonicity assumption we present methods for both pairwise and joint causal analyses of ordinal treatments and binary outcomes in the presence of truncation by death. We illustrate via ex...
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作者：Bradley, Jonathan R.; Wikle, Christopher K.; Holan, Scott H.
作者单位：University of Missouri System; University of Missouri Columbia
摘要：The modifiable areal unit problem and the ecological fallacy are known problems that occur when modelling multiscale spatial processes. We investigate how these forms of spatial aggregation error can guide a regionalization over a spatial domain of interest. By regionalization' we mean a specification of geographies that define the spatial support for areal data. This topic has been studied vigorously by geographers but has been given less attention by spatial statisticians. Thus, we propose a...
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作者：Passemier, Damien; Li, Zhaoyuan; Yao, Jianfeng
作者单位：University of Hong Kong
摘要：We develop new statistical theory for probabilistic principal component analysis models in high dimensions. The focus is the estimation of the noise variance, which is an important and unresolved issue when the number of variables is large in comparison with the sample size. We first unveil the reasons for an observed downward bias of the maximum likelihood estimator of the noise variance when the data dimension is high. We then propose a bias-corrected estimator by using random-matrix theory ...
