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作者：Xia, Yingcun
作者单位：National University of Singapore
摘要：Dimension reduction can be used as an initial step in statistical modeling. Further specification of model structure is imminent and important when the reduced dimension is still greater than 1. In this article we investigate one method of specification that involves separating the linear component front the nonlinear components, leading to further dimension reduction in the unknown link function and. thus, better estimation and easier interpretation of the model. The specified Model includes ...
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作者：Ibrahim, Joseph G.; Zhu, Hongtu; Tang, Niansheng
作者单位：University of North Carolina; University of North Carolina Chapel Hill; Yunnan University
摘要：We consider novel methods for the Computation of model selection criteria in missing-data problems based on the output of the EM algorithm The methodology is very general and can be applied to numerous simulations involving incomplete data within an EM framework, from covariates missing at random in arbitrary regression models to nonignorably missing longitudinal responses and/or covariates. Toward this goal, we develop a class of information criteria for missing-data problems called ICH,Q, wh...
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作者：Chen, Jiahua; Khalili, Abbas
作者单位：University of British Columbia
摘要：Order selection is a fundamental and challenging problem in the application of finite mixture models. We develop a new penalized likelihood approach that we call MSCAD. MSCAD deviates from information-based methods, such as Akaike information criterion and the Bayes information criterion, by introducing two penalty functions that depend on the mixing proportions and the component parameters. It is consistent in estimating both the order of the mixture model and the mixing distribution. Simulat...
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作者：Wang, Lifeng; Li, Hongzhe; Huang, Jianhua Z.
作者单位：University of Pennsylvania; Texas A&M University System; Texas A&M University College Station
摘要：Nonparametric varying-coefficient models are commonly used for analyzing data measured repeatedly over time, including longitudinal and functional response data. Although many procedures have been developed for estimating varying coefficients. the problem of variable selection for such models has rot been addressed to date. la this article we present a regularized estimation procedure for variable selection that combines basis function approximations and the smoothly clipped absolute deviation...
