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作者：Dai, Chenguang; Heng, Jeremy; Jacob, Pierre E.; Whiteley, Nick
作者单位：ESSEC Business School; University of Bristol
摘要：Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo samplers are a class of algorithms that combine both techniques to approximate distributions of interest and their normalizing constants. These samplers originate from particle filtering for state space models and have become general and scalable sampling techniques. This article describes sequential Monte Carlo samplers a...
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作者：Brown, D. Andrew; McMahan, Christopher S.; Shinohara, Russell T.; Linn, Kristin A.
作者单位：Clemson University; University of Pennsylvania; University of Pennsylvania
摘要：Alzheimer's disease is a neurodegenerative condition that accelerates cognitive decline relative to normal aging. It is of critical scientific importance to gain a better understanding of early disease mechanisms in the brain to facilitate effective, targeted therapies. The volume of the hippocampus is often used in diagnosis and monitoring of the disease. Measuring this volume via neuroimaging is difficult since each hippocampus must either be manually identified or automatically delineated, ...
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作者：Li, Sai; Cai, T. Tony; Li, Hongzhe
作者单位：University of Pennsylvania; University of Pennsylvania
摘要：Linear mixed-effects models are widely used in analyzing clustered or repeated measures data. We propose a quasi-likelihood approach for estimation and inference of the unknown parameters in linear mixed-effects models with high-dimensional fixed effects. The proposed method is applicable to general settings where the dimension of the random effects and the cluster sizes are possibly large. Regarding the fixed effects, we provide rate optimal estimators and valid inference procedures that do n...
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作者：Zammit-Mangion, Andrew; Ng, Tin Lok James; Vu, Quan; Filippone, Maurizio
作者单位：University of Wollongong; IMT - Institut Mines-Telecom; EURECOM
摘要：Spatial processes with nonstationary and anisotropic covariance structure are often used when modeling, analyzing, and predicting complex environmental phenomena. Such processes may often be expressed as ones that have stationary and isotropic covariance structure on a warped spatial domain. However, the warping function is generally difficult to fit and not constrained to be injective, often resulting in space-folding. Here, we propose modeling an injective warping function through a composit...
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作者：Deb, Nabarun; Saha, Sujayam; Guntuboyina, Adityanand; Sen, Bodhisattva
作者单位：Columbia University; Alphabet Inc.; Google Incorporated; University of California System; University of California Berkeley
摘要：In this article, we study a generalization of the two-groups model in the presence of covariates-a problem that has recently received much attention in the statistical literature due to its applicability in multiple hypotheses testing problems. The model we consider allows for infinite dimensional parameters and offers flexibility in modeling the dependence of the response on the covariates. We discuss the identifiability issues arising in this model and systematically study several estimation...
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作者：Daouia, Abdelaati; Gijbels, Irene; Stupfler, Gilles
作者单位：Universite de Toulouse; Universite Toulouse 1 Capitole; Toulouse School of Economics; KU Leuven; Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI)
摘要：Regression extremiles define a least squares analogue of regression quantiles. They are determined by weighted expectations rather than tail probabilities. Of special interest is their intuitive meaning in terms of expected minima and maxima. Their use appears naturally in risk management where, in contrast to quantiles, they fulfill the coherency axiom and take the severity of tail losses into account. In addition, they are comonotonically additive and belong to both the families of spectral ...
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作者：Cai, T. Tony; Sun, Wenguang; Xia, Yin
作者单位：University of Pennsylvania; University of Southern California; Fudan University
摘要：Exploiting spatial patterns in large-scale multiple testing promises to improve both power and interpretability of false discovery rate (FDR) analyses. This article develops a new class of locally adaptive weighting and screening (LAWS) rules that directly incorporates useful local patterns into inference. The idea involves constructing robust and structure-adaptive weights according to the estimated local sparsity levels. LAWS provides a unified framework for a broad range of spatial problems...
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作者：Jochmans, Koen
作者单位：University of Cambridge
摘要：We consider inference in linear regression models that is robust to heteroscedasticity and the presence of many control variables. When the number of control variables increases at the same rate as the sample size the usual heteroscedasticity-robust estimators of the covariance matrix are inconsistent. Hence, tests based on these estimators are size distorted even in large samples. An alternative covariance-matrix estimator for such a setting is presented that complements recent work by Cattan...
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作者：Li, Tianxi; Lei, Lihua; Bhattacharyya, Sharmodeep; Van den Berge, Koen; Sarkar, Purnamrita; Bickel, Peter J.; Levina, Elizaveta
作者单位：University of Virginia; Stanford University; Oregon State University; University of California System; University of California Berkeley; Ghent University; University of Texas System; University of Texas Austin; University of Michigan System; University of Michigan
摘要：The problem of community detection in networks is usually formulated as finding a single partition of the network into some correct number of communities. We argue that it is more interpretable and in some regimes more accurate to construct a hierarchical tree of communities instead. This can be done with a simple top-down recursive partitioning algorithm, starting with a single community and separating the nodes into two communities by spectral clustering repeatedly, until a stopping rule sug...
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作者：Stensrud, Mats J.; Young, Jessica G.; Didelez, Vanessa; Robins, James M.; Hernan, Miguel A.
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; University of Oslo; Harvard University; Harvard Medical School; Harvard Pilgrim Health Care; Leibniz Association; Leibniz Institute for Prevention Research & Epidemiology (BIPS); University of Bremen; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University
摘要：In time-to-event settings, the presence of competing events complicates the definition of causal effects. Here we propose the new separable effects to study the causal effect of a treatment on an event of interest. The separable direct effect is the treatment effect on the event of interest not mediated by its effect on the competing event. The separable indirect effect is the treatment effect on the event of interest only through its effect on the competing event. Similar to Robins and Richar...
