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作者：Wei, Susan; Kosorok, Michael R.
作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
摘要：This article introduces a new machine learning task, called latent supervised learning, where the goal is to learn a binary classifier from continuous training labels that serve as surrogates for the unobserved class labels. We investigate a specific model where the surrogate variable arises from a two-component Gaussian mixture with unknown means and variances, and the component membership is determined by a hyperplane in the covariate space. The estimation of the separating hyperplane and th...
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作者：Galvao, Antonio F.; Lamarche, Carlos; Lima, Luiz Renato
作者单位：University of Iowa; University of Kentucky; University of Tennessee System; University of Tennessee Knoxville; Universidade Federal da Paraiba
摘要：This article investigates estimation of censored quantile regression (QR) models with fixed effects. Standard available methods are not appropriate for estimation of a censored QR model with a large number of parameters or with covariates correlated with unobserved individual heterogeneity. Motivated by these limitations, the article proposes estimators that are obtained by applying fixed effects QR to subsets of observations selected either parametrically or nonparametrically. We derive the l...
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作者：Majumder, Mahbubul; Hofmann, Heike; Cook, Dianne
作者单位：Iowa State University
摘要：Statistical graphics play a crucial role in exploratory data analysis, model checking, and diagnosis. The lineup protocol enables statistical significance testing of visual findings, bridging the gulf between exploratory and inferential statistics. In this article, inferential methods for statistical graphics are developed further by refining the terminology of visual inference and framing the lineup protocol in a context that allows direct comparison with conventional tests in scenarios when ...
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作者：Fan, Yingying; Lv, Jinchi
作者单位：University of Southern California
摘要：High-dimensional data analysis has motivated a spectrum of regularization methods for variable selection and sparse modeling, with two popular methods being convex and concave ones. A long debate has taken place on whether one class dominates the other, an important question both in theory and to practitioners. In this article, we characterize the asymptotic equivalence of regularization methods, with general penalty functions, in a thresholded parameter space under the generalized linear mode...
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作者：Gilbert, Peter B.; Shepherd, Bryan E.; Hudgens, Michael G.
作者单位：University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center; Vanderbilt University; University of North Carolina; University of North Carolina Chapel Hill
摘要：Assessing per-protocol (PP) treatment efficacy on a time-to-event endpoint is a common objective of randomized clinical trials. The typical analysis uses the same method employed for the intention-to-treat analysis (e.g., standard survival analysis) applied to the subgroup meeting protocol adherence criteria. However, due to potential post-randomization selection bias, this analysis may mislead about treatment efficacy. Moreover, while there is extensive literature on methods for assessing cau...
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作者：Mealli, Fabrizia; Pacini, Barbara
作者单位：University of Florence; University of Pisa
摘要：We develop new methods for analyzing randomized experiments with noncompliance and, by extension, instrumental variable settings, when the often controversial, but key, exclusion restriction assumption is violated. We show how existing large-sample bounds on intention-to-treat effects for the subpopulations of compliers, never-takers, and always-takers can be tightened by exploiting the joint distribution of the outcome of interest and a secondary outcome, for which the exclusion restriction i...
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作者：Stingo, Francesco C.; Guindani, Michele; Vannucci, Marina; Calhoun, Vince D.
作者单位：University of Texas System; UTMD Anderson Cancer Center; Rice University; University of New Mexico; University of New Mexico; University of New Mexico; University of New Mexico
摘要：In this article we present a Bayesian hierarchical modeling approach for imaging genetics, where the interest lies in linking brain connectivity across multiple individuals to their genetic information. We have available data from a functional magnetic resonance imaging (fMRI) study on schizophrenia. Our goals are to identify brain regions of interest (ROIs) with discriminating activation patterns between schizophrenic patients and healthy controls, and to relate the ROIs' activations with ava...
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作者：Lee, Juhee; Mueller, Peter; Zhu, Yitan; Ji, Yuan
作者单位：University System of Ohio; Ohio State University; University of Texas System; University of Texas Austin; NorthShore University Health System
摘要：We propose a nonparametric Bayesian local clustering (NoB-LoC) approach for heterogeneous data. NoB-LoC implements inference for nested clusters as posterior inference under a Bayesian model. Using protein expression data as an example, the NoB-LoC model defines a protein (column) cluster as a set of proteins that give rise to the same partition of the samples (rows). In other words, the sample partitions are nested within protein clusters. The common clustering of the samples gives meaning to...
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作者：Paindaveine, Davy; Van Bever, Germain
作者单位：Universite Libre de Bruxelles; Universite Libre de Bruxelles
摘要：Aiming at analyzing multimodal or nonconvexly supported distributions through data depth, we introduce a local extension of depth. Our construction is obtained by conditioning the distribution to appropriate depth-based neighborhoods and has the advantages, among others, of maintaining affine-invariance and applying to all depths in a generic way. Most importantly, unlike their competitors, which (for extreme localization) rather measure probability mass, the resulting local depths focus on ce...
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作者：Reich, Brian J.; Bandyopadhyay, Dipankar; Bondell, Howard D.
作者单位：North Carolina State University; University of Minnesota System; University of Minnesota Twin Cities
摘要：Periodontal disease (PD) progression is often quantified by clinical attachment level (CAL) defined as the distance down a tooth's root that is detached from the surrounding bone. Measured at six locations per tooth throughout the mouth (excluding the molars), it gives rise to a dependent data setup. These data are often reduced to a one-number summary, such as the whole-mouth average or the number of observations greater than a threshold, to be used as the response in a regression to identify...
