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作者：Fischer, Lasse; Roig, Marta Bofill; Brannath, Werner
作者单位：University of Bremen; Medical University of Vienna
摘要：The closure principle is fundamental in multiple testing and has been used to derive many efficient procedures with familywise error rate control. However, it is often unsuitable for modern research, which involves flexible multiple testing settings where not all hypotheses are known at the beginning of the evaluation. In this paper, we focus on online multiple testing where a possibly infinite sequence of hypotheses is tested over time. At each step, it must be decided on the current hypothes...
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作者：Stankewitz, Bernhard
作者单位：Bocconi University
摘要：Increasingly high-dimensional data sets require that estimation methods do not only satisfy statistical guarantees but also remain computationally feasible. In this context, we consider L2-boosting via orthogonal matching pursuit in a high-dimensional linear model and analyze a data-driven early stopping time tau of the algorithm, which is sequential in the sense that its computation is based on the first tau iterations only. This approach is much less costly than established model selection c...
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作者：Van Delft, Anne; Dette, Holger
作者单位：Columbia University; Ruhr University Bochum
摘要：We present a general theory to quantify the uncertainty from imposing structural assumptions on the second-order structure of nonstationary Hilbert space-valued processes, which can be measured via functionals of time-dependent spectral density operators. The second-order dynamics are well known to be elements of the space of trace class operators, the latter is a Banach space of type 1 and of cotype 2, which makes the development of statistical inference tools more challenging. A part of our ...
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作者：Chang, Jinyuan; Hu, Qiao; Kolaczyk, Eric d.; Yao, Qiwei; Yi, Fengting
作者单位：Southwestern University of Finance & Economics - China; Chinese Academy of Sciences; McGill University; University of London; London School Economics & Political Science; Yunnan University
摘要：A standing challenge in data privacy is the trade-off between the level of privacy and the efficiency of statistical inference. Here, we conduct an indepth study of this trade-off for parameter estimation in the beta-model (Ann. Appl. Probab. 21 (2011) 1400-1435) for edge differentially private network 500). Unlike most previous approaches based on maximum likelihood estimation for this network model, we proceed via the method of moments. This choice facilitates our exploration of a substantia...
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作者：Soloff, Jake a.; Xiang, Daniel; Fithian, William
作者单位：University of Chicago; University of California System; University of California Berkeley
摘要：Despite the popularity of the false discovery rate (FDR) as an error control metric for large-scale multiple testing, its close Bayesian counterpart the local false discovery rate (lfdr), defined as the posterior probability that a particular null hypothesis is false, is a more directly relevant standard for justifying and interpreting individual rejections. However, the lfdr is difficult to work with in small samples, as the prior distribution is typically unknown. We propose a simple multipl...
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作者：Pilipovic, Predrag; Samson, Adeline; Ditlevsen, Susanne
作者单位：University of Copenhagen; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA)
摘要：The likelihood functions for discretely observed nonlinear continuous time models based on stochastic differential equations are not available except for a few cases. Various parameter estimation techniques have been proposed, each with advantages, disadvantages and limitations depending on the application. Most applications still use the Euler-Maruyama discretization, despite many proofs of its bias. More sophisticated methods, such as Hermite expansions or MCMC methods, might be complex to i...
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作者：Li, Jiaqi; Chen, Likai; Wang, Weining; Wu, Wei biao
作者单位：Washington University (WUSTL); University of Groningen; University of Chicago
摘要：We propose an inference method for detecting multiple change points inhigh-dimensional time series, targeting dense or spatially clustered signals.Our method aggregates moving sum (MOSUM) statistics cross-sectionallyby an2-norm and maximizes them over time. We further introduce anovel Two-Way MOSUM, which utilizes spatial-temporal moving regionsto search for breaks, with the added advantage of enhancing testing powerwhen breaks occur in only a few groups. The limiting distribution of an2-aggre...
