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作者：Yuan, M; Lin, Y
作者单位：University System of Georgia; Georgia Institute of Technology; University of Wisconsin System; University of Wisconsin Madison
摘要：We propose an empirical Bayes method for variable selection and coefficient estimation in linear regression models. The method is based on a particular hierarchical Bayes formulation, and the empirical Bayes estimator is shown to be closely related to the LASSO estimator. Such a connection allows us to take advantage of the recently developed quick LASSO algorithm to compute the empirical Bayes estimate, and provides a new way to select the tuning parameter in the LASSO method. Unlike previous...
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作者：He, XM; Fung, WK; Zhu, ZY
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Hong Kong; East China Normal University
摘要：In this article we consider robust generalized estimating equations for the analysis of semiparametric generalized partial linear models (GPLMs) for longitudinal data or clustered data in general. We approximate the nonparametric function in the GPLM by a regression spline, and use bounded scores and leverage-based weights in the estimating equation to achieve robustness against outliers. We show that the regression spline approach avoids some of the intricacies associated with the profile-ker...
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作者：Louis, TA
作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
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作者：Wolbers, M; Stahel, W
作者单位：Roche Holding; Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：In many fields of science there are multivariate observations that may be assumed to be generated by a (physical) linear mixing process of contributions from different sources. If the compositions of the sources are constant for different observations, then these observations are, up to a random error term, nonnegative linear combinations of a fixed set of so-called source profiles that characterize the sources. The goal of linear unmixing is to recover both the source profiles and the source ...
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作者：Lijoi, A; Mena, RH; Prünster, I
作者单位：University of Pavia; Consiglio Nazionale delle Ricerche (CNR); Universidad Nacional Autonoma de Mexico
摘要：In recent years the Dirichlet process prior has experienced a great success in the context of Bayesian mixture modeling. The idea of overcoming discreteness of its realizations by exploiting it in hierarchical models, combined with the development of suitable sampling techniques, represent one of the reasons of its popularity. In this article we propose the normalized inverse-Gaussian (N-IG) process as an alternative to the Dirichlet process to be used in Bayesian hierarchical models. The N-IG...
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作者：Song, PXK; Fan, YQ; Kalbfleisch, JD
作者单位：University of Waterloo; Vanderbilt University; University of Michigan System; University of Michigan
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作者：Zhang, L; Mykland, PA; Aït-Sahalia, Y
作者单位：Carnegie Mellon University; University of Chicago; Princeton University; Princeton University; National Bureau of Economic Research
摘要：It is a common practice in finance to estimate volatility from the sum of frequently sampled squared returns. However, market microstructure poses challenges to this estimation approach, as evidenced by recent empirical studies in finance. The present work attempts to lay out theoretical grounds that reconcile continuous-time modeling and discrete-time samples. We propose an estimation approach that takes advantage of the rich sources in tick-by-tick data while preserving the continuous-time a...
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作者：Lin, MT; Zhang, JNL; Cheng, QS; Chen, R
作者单位：Peking University; Peking University; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
摘要：Sequential Monte Carlo methods, especially the particle filter (PF) and its various modifications, have been used effectively in dealing with stochastic dynamic systems. The standard PF samples the current state through the underlying state dynamics, then uses the current observation to evaluate the sample's importance weight. However, there is a set of problems in which the current observation provides significant information about the current state but the state dynamics are weak, and thus s...
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作者：Sánchez, BN; Budtz-Jorgensen, E; Ryan, LM; Hu, H
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; University of Copenhagen
摘要：Structural equation models (SEMs) have been discussed extensively in the psychometrics and quantitative behavioral sciences literature. However, many statisticians and researchers in other areas of application are relatively unfamiliar with their implementation. Here we review some of the SEM literature and describe basic methods, using examples from environmental epidemiology. We make connections to recent work on latent variable models for multivariate outcomes and to measurement error metho...
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作者：Song, PXK; Fan, YQ; Kalbfleisch, JD
作者单位：University of Waterloo; Vanderbilt University; University of Michigan System; University of Michigan
摘要：This article presents and examines a new algorithm for solving a score equation for the maximum likelihood estimate in certain problems of practical interest. The method circumvents the need to compute second-order derivatives of the full likelihood function. It exploits the structure of certain models that yield a natural decomposition of a very complicated likelihood function. In this decomposition, the first part is a log-likelihood from a simply analyzed model, and the second part is used ...
