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作者：Li, Jialiang; Lv, Jing; Wan, Alan T. K.; Liao, Jun
作者单位：National University of Singapore; Southwest University - China; City University of Hong Kong; Renmin University of China
摘要：Model average techniques are very useful for model-based prediction. However, most earlier works in this field focused on parametric models and continuous responses. In this article, we study varying coefficient multinomial logistic models and propose a semiparametric model averaging prediction (SMAP) approach for multi-category outcomes. The proposed procedure does not need any artificial specification of the index variable in the adopted varying coefficient sub-model structure to forecast th...
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作者：Yu, Jiahui; Shi, Jian; Liu, Anna; Wang, Yuedong
作者单位：Boston University; PayPal Holdings, Inc.; University of Massachusetts System; University of Massachusetts Amherst; University of California System; University of California Santa Barbara
摘要：Density estimation plays a fundamental role in many areas of statistics and machine learning. Parametric, nonparametric, and semiparametric density estimation methods have been proposed in the literature. Semiparametric density models are flexible in incorporating domain knowledge and uncertainty regarding the shape of the density function. Existing literature on semiparametric density models is scattered and lacks a systematic framework. In this article, we consider a unified framework based ...
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作者：Zhang, Xianyang; Chen, Jun
作者单位：Texas A&M University System; Texas A&M University College Station; Mayo Clinic; Mayo Clinic
摘要：Conventional multiple testing procedures often assume hypotheses for different features are exchangeable. However, in many scientific applications, additional covariate information regarding the patterns of signals and nulls are available. In this article, we introduce an FDR control procedure in large-scale inference problem that can incorporate covariate information. We develop a fast algorithm to implement the proposed procedure and prove its asymptotic validity even when the underlying lik...
