525期

最新文章

• Optimal Subsampling for Large Sample Logistic Regression (vol 113, pg 829, 2018) AI翻译
  作者：Wang, Haiying; Zhu, Rong; Ma, Ping
  作者单位：University System of Georgia; University of Georgia
• Group SLOPE - Adaptive Selection of Groups of Predictors AI翻译
  作者：Brzyski, Damian; Gossmann, Alexej; Su, Weijie; Bogdan, Malgorzata
  作者单位：Indiana University System; Indiana University Bloomington; Jagiellonian University; Tulane University; University of Pennsylvania; University of Wroclaw
  摘要：Sorted L-One Penalized Estimation (SLOPE; Bogdan etal. 2013, 2015) is a relatively new convex optimization procedure, which allows for adaptive selection of regressors under sparse high-dimensional designs. Here, we extend the idea of SLOPE to deal with the situation when one aims at selecting whole groups of explanatory variables instead of single regressors. Such groups can be formed by clustering strongly correlated predictors or groups of dummy variables corresponding to different levels o...
• Changepoint Detection in the Presence of Outliers AI翻译
  作者：Fearnhead, Paul; Rigaill, Guillem
  作者单位：Lancaster University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Biology (INSB); INRAE; Universite Paris Saclay; Universite Paris Cite; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Universite Paris Saclay; INRAE; Ecole Nationale Superieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)
  摘要：Many traditional methods for identifying changepoints can struggle in the presence of outliers, or when the noise is heavy-tailed. Often they will infer additional changepoints to fit the outliers. To overcome this problem, data often needs to be preprocessed to remove outliers, though this is difficult for applications where the data needs to be analyzed online. We present an approach to changepoint detection that is robust to the presence of outliers. The idea is to adapt existing penalized ...
• Robust Variable and Interaction Selection for Logistic Regression and General Index Models AI翻译
  作者：Li, Yang; Liu, Jun S.
  作者单位：Harvard University; Tsinghua University
  摘要：Under the logistic regression framework, we propose a forward-backward method, SODA, for variable selection with both main and quadratic interaction terms. In the forward stage, SODA adds in predictors that have significant overall effects, whereas in the backward stage SODA removes unimportant terms to optimize the extended Bayesian information criterion (EBIC). Compared with existing methods for variable selection in quadratic discriminant analysis, SODA can deal with high-dimensional data i...
• Comment: Penalized Spline of Propensity Methods for Treatment Comparison AI翻译
  作者：Chen, Qingxia; Harrell, Frank E., Jr.
  作者单位：Vanderbilt University
• Information-Based Optimal Subdata Selection for Big Data Linear Regression AI翻译
  作者：Wang, HaiYing; Yang, Min; Stufken, John
  作者单位：University of Connecticut; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Arizona State University; Arizona State University-Tempe
  摘要：Extraordinary amounts of data are being produced in many branches of science. Proven statistical methods are no longer applicable with extraordinary large datasets due to computational limitations. A critical step in big data analysis is data reduction. Existing investigations in the context of linear regression focus on subsampling-based methods. However, not only is this approach prone to sampling errors, it also leads to a covariance matrix of the estimators that is typically bounded from b...
• Penalized Spline of Propensity Methods for Treatment Comparison AI翻译
  作者：Zhou, Tingting; Elliott, Michael R.; Little, Roderick J. A.
  作者单位：University of Michigan System; University of Michigan
  摘要：Valid causal inference from observational studies requires controlling for confounders. When time-dependent confounders are present that serve as mediators of treatment effects and affect future treatment assignment, standard regression methods for controlling for confounders fail. Similar issues also arise in trials with sequential randomization, when randomization at later time points is based on intermediate outcomes from earlier randomized assignments. We propose a robust multiple imputati...
• Adaptive Bayesian Time-Frequency Analysis of Multivariate Time Series AI翻译
  作者：Li, Zeda; Krafty, Robert T.
  作者单位：City University of New York (CUNY) System; Baruch College (CUNY); Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
  摘要：This article introduces a nonparametric approach to multivariate time-varying power spectrum analysis. The procedure adaptively partitions a time series into an unknown number of approximately stationary segments, where some spectral components may remain unchanged across segments, allowing components to evolve differently over time. Local spectra within segments are fit through Whittle likelihood-based penalized spline models of modified Cholesky components, which provide flexible nonparametr...
• Discussion of Penalized Spline of Propensity Methods for Treatment Comparison AI翻译
  作者：Papadogeorgou, Georgia; Li, Fan
  作者单位：Duke University
• Constructing Priors that Penalize the Complexity of Gaussian Random Fields AI翻译
  作者：Fuglstad, Geir-Arne; Simpson, Daniel; Lindgren, Finn; Rue, Havard
  作者单位：Norwegian University of Science & Technology (NTNU); University of Toronto; University of Edinburgh; King Abdullah University of Science & Technology
  摘要：Priors are important for achieving proper posteriors with physically meaningful covariance structures for Gaussian random fields (GRFs) since the likelihood typically only provides limited information about the covariance structure under in-fill asymptotics. We extend the recent penalized complexity prior framework and develop a principled joint prior for the range and the marginal variance of one-dimensional, two-dimensional, and three-dimensional Matern GRFs with fixed smoothness. The prior ...