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作者：Katenka, Natallia; Levina, Elizaveta; Michailidis, George
作者单位：University of Rhode Island; University of Michigan System; University of Michigan
摘要：This article introduces a framework for tracking multiple targets over time using binary decisions collected by a wireless sensor network, and applies the methodology to two case studies-an experiment involving tracking people and a dataset adapted from a project tracking zebras in Kenya. The tracking approach is based on a penalized maximum likelihood framework, and allows for sensor failures, targets appearing and disappearing over time, and complex intersecting target trajectories. We show ...
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作者：Liu, Rong; Yang, Lijian; Haerdle, Wolfgang K.
作者单位：Soochow University - China; University System of Ohio; University of Toledo; Michigan State University; Humboldt University of Berlin
摘要：The generalized additive model (GAM) is a multivariate nonparametric regression tool for non-Gaussian responses including binary and count data. We propose a spline-backfitted kernel (SBK) estimator for the component functions and the constant, which are oracally efficient under weak dependence. The SBK technique is both computationally expedient and theoretically reliable, thus usable for analyzing high-dimensional time series. Inference can be made on component functions based on asymptotic ...
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作者：Wang, Xueqin; Jiang, Yunlu; Huang, Mian; Zhang, Heping
作者单位：Sun Yat Sen University; Sun Yat Sen University; Sun Yat Sen University; Shanghai University of Finance & Economics; Yale University
摘要：Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this article, we propose a class of penalized robust regression estimators based on exponential squared loss. The motivation for this new procedure is th...
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作者：Mitra, Riten; Mueller, Peter; Liang, Shoudan; Yue, Lu; Ji, Yuan
作者单位：University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center; NorthShore University Health System
摘要：Histone modifications (HMs) are an important post-translational feature. Different types of HMs are believed to co-exist and co-regulate biological processes such as gene expression and, therefore, are intrinsically dependent on each other. We develop inference for this complex biological network of HMs based on a graphical model using ChIP-Seq data. A critical computational hurdle in the inference for the proposed graphical model is the evaluation of a normalization constant in an autologisti...
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作者：Li, Wentao; Tan, Zhiqiang; Chen, Rong
作者单位：Lancaster University; Rutgers University System; Rutgers University New Brunswick
摘要：For importance sampling (IS), multiple proposals can be combined to address different aspects of a target distribution. There are various methods for IS with multiple proposals, including Hesterberg's stratified IS estimator, Owen and Zhou's regression estimator, and Tan's maximum likelihood estimator. For the problem of efficiently allocating samples to different proposals, it is natural to use a pilot sample to select the mixture proportions before the actual sampling and estimation. However...
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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：Fan, Chunpeng; Fine, Jason P.
作者单位：Sanofi-Aventis; Sanofi USA; University of North Carolina; University of North Carolina Chapel Hill
摘要：The traditional linear transformation model assumes a linear relationship between the transformed response and the covariates. However, in real data, this linear relationship may be violated. We propose a linear transformation model that allows parametric covariate transformations to recover the linearity. Although the proposed generalization may seem rather simple, the inferential issues are quite challenging due to loss of identifiability under the null of no effects of transformed covariate...
