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作者：Wu, Yujun; Boos, Dennis D.; Stefanski, Leonard A.
作者单位：Rutgers University System; Rutgers University New Brunswick; Rutgers University Biomedical & Health Sciences; North Carolina State University
摘要：We propose a new approach to variable selection designed to control the false selection rate (FSR), defined as the proportion of uninformative variables included in selected models. The method works by adding a known number of pseudovariables to the real dataset, running a variable selection procedure, and monitoring the proportion of pseudovariables falsely selected. Information obtained from bootstrap-like replications of this process is used to estimate the proportion of falsely selected re...
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作者：Wang, Huixia; He, Xuming
作者单位：North Carolina State University; University of Illinois System; University of Illinois Urbana-Champaign
摘要：In this article we consider testing for differentially expressed genes in GeneChip studies by modeling and analyzing the quantiles of gene expression through probe level measurements. By developing a robust rank score test for linear quantile models with a random effect, we propose a reliable test for detecting differences in certain quantiles of the intensity distributions. By using a genomewide adjustment to the test statistic to account for within-array correlation, we demonstrate that the ...
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作者：Bortot, P.; Coles, S. G.; Sisson, S. A.
作者单位：University of Bologna; University of Padua; University of New South Wales Sydney
摘要：In the production of clean steels, the occurrence of imperfections-so-called inclusions-is unavoidable. The strength of a clean steel block is largely dependent on the size of the largest imperfection that it contains, so inference on extreme inclusion size forms an important part of quality control. Sampling is generally done by measuring imperfections on planar slices, leading to an extreme value version of a standard stereological problem: how to make inference on large inclusions using onl...
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作者：Naik, Prasad A.; Shi, Peide; Tsai, Chih-Ling
作者单位：University of California System; University of California Davis; Peking University
摘要：We examine the problem of jointly selecting the number of components and variables in finite mixture regression models. We find that the Akaike information criterion is unsatisfactory for this purpose because it overestimates the number of components, which in turn results in incorrect variables being retained in the model. Therefore, we derive a new information criterion, the mixture regression criterion (MRC), that yields marked improvement in model selection due to what we call the clusteri...
