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作者：Belloni, Alexandre; Chernozhukov, Victor
作者单位：Duke University; Massachusetts Institute of Technology (MIT)
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作者：Sewell, Daniel K.; Chen, Yuguo
作者单位：University of Illinois System; University of Illinois Urbana-Champaign
摘要：Dynamic networks are used in a variety of fields to represent the structure and evolution of the relationships between entities. We present a model which embeds longitudinal network data as trajectories in a latent Euclidean space. We propose Markov chain Monte Carlo (MCMC) algorithm to estimate the model parameters and latent positions of the actors in the network. The model yields meaningful visualization of dynamic networks, giving the researcher insight into the evolution and the structure...
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作者：Zhu, Ruoqing; Zeng, Donglin; Kosorok, Michael R.
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：In this article, we introduce a new type of tree-based method, reinforcement learning trees (RLT), which exhibits significantly improved performance over traditional methods such as random forests (Breiman 2001) under high-dimensional settings. The innovations are threefold. First, the new method implements reinforcement learning at each selection of a splitting variable during the tree construction processes. By splitting on the variable that brings the greatest future improvement in later sp...
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作者：Azzimonti, Laura; Sangalli, Laura M.; Secchi, Piercesare; Domanin, Maurizio; Nobile, Fabio
作者单位：Polytechnic University of Milan; IRCCS Ca Granda Ospedale Maggiore Policlinico; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
摘要：We propose an innovative method for the accurate estimation of surfaces and spatial fields when prior knowledge of the phenomenon under study is available. The prior knowledge included in the model derives from physics, physiology, or mechanics of the problem at hand, and is formalized in terms of a partial differential equation governing the phenomenon behavior, as well as conditions that the phenomenon has to satisfy at the boundary of the problem domain. The proposed models exploit advanced...
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作者：Barney, Bradley J.; Amici, Federica; Aureli, Filippo; Call, Josep; Johnson, Valen E.
作者单位：University System of Georgia; Kennesaw State University; Max Planck Society; Universidad Veracruzana; Liverpool John Moores University; University of St Andrews; Max Planck Society
摘要：In recent years, substantial effort has been devoted to methods for analyzing data containing mixed response types, but such techniques typically do not include rank data among the response types. Some unique challenges exist in analyzing rank data, particularly when ties are prevalent. We present techniques for jointly modeling binomial and rank data using Bayesian latent variable models. We apply these techniques to compare the cognitive abilities of nonhuman primates based on their performa...
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作者：Chiou, Sy Han; Kang, Sangwook; Yan, Jun
作者单位：University of Minnesota System; University of Minnesota Duluth; Yonsei University; University of Connecticut; University of Connecticut; University of Connecticut
摘要：Clustered failure times often arise from studies with stratified sampling designs where it is desired to reduce both cost and sampling error. Semiparametric accelerated failure time (AFT) models have not been used as frequently as Cox relative risk models in such settings due to lack of efficient and reliable computing routines for inferences. The challenge roots in the nonsmoothness of the rank-based estimating functions, and for clustered data, the asymptotic properties of the estimator from...
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作者：Du, Jiejun; Dryden, Ian L.; Huang, Xianzheng
作者单位：University of Nottingham; University of South Carolina System; University of South Carolina Columbia
摘要：We consider the problem of comparing sizes and shapes of objects when landmark data are prone to measurement error. We show that naive implementation of ordinary Procrustes analysis that ignores measurement error can compromise inference. To account for measurement error, we propose the conditional score method for matching configurations, which guarantees consistent inference under mild model assumptions. The effects of measurement error on inference from naive Procrustes analysis and the per...
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作者：Angrist, Joshua D.; Rokkanen, Miikka
作者单位：Massachusetts Institute of Technology (MIT); National Bureau of Economic Research; Columbia University
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作者：Wang, Lan; Peng, Bo; Li, Runze
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Minnesota System; University of Minnesota Twin Cities; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：This work is concerned with testing the population mean vector of nonnormal high-dimensional multivariate data. Several tests for high-dimensional mean vector, based on modifying the classical Hotelling T-2 test, have been proposed in the literature. Despite their usefulness, they tend to have unsatisfactory power performance for heavy-tailed multivariate data, which frequently arise in genomics and quantitative finance. This article proposes a novel high-dimensional nonparametric test for the...
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作者：Wang, Liangliang; Bouchard-Cote, Alexandre; Doucet, Arnaud
作者单位：Simon Fraser University; University of British Columbia; University of Oxford
摘要：The application of Bayesian methods to large-scale phylogenetics problems is increasingly limited by computational issues, motivating the development of methods that can complement existing Markov chain Monte Carlo (MCMC) schemes. Sequential Monte Carlo (SMC) methods are approximate inference algorithms that have become very popular for time series models. Such methods have been recently developed to address phylogenetic inference problems but currently available techniques are only applicable...
