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作者：She, Yiyuan; Li, Shijie; Wu, Dapeng
作者单位：State University System of Florida; Florida State University
摘要：Recently, the robustification of principal component analysis (PCA) has attracted lots of attention from statisticians, engineers, and computer scientists. In this work, we study the type of outliers that are not necessarily apparent in the original observation space but can seriously affect the principal sub-space estimation. Based on a mathematical formulation of such transformed outliers, a novel robust orthogonal complement principal component analysis (ROC-PCA) is proposed. The framework ...
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作者：Tibshirani, Ryan J.; Taylor, Jonathan; Lockhart, Richard; Tibshirani, Robert
作者单位：Carnegie Mellon University; Carnegie Mellon University
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作者：Yu, Guan; Liu, Yufeng
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：With the abundance of high-dimensional data in various disciplines, sparse regularized techniques are very popular these days. In this article, we make use of the structure information among predictors to improve sparse regression models. Typically, such structure information can be modeled by the connectivity of an undirected graph using all predictors as nodes of the graph. Most existing methods use this undirected graph edge-by-edge to encourage the regression coefficients of corresponding ...
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作者：Minsker, Stanislav; Zhao, Ying-Qi; Cheng, Guang
作者单位：University of Wisconsin System; University of Wisconsin Madison
摘要：Individualized treatment rules (ITRs) tailor treatments according to individual patient characteristics. They can significantly improve patient care and are thus becoming increasingly popular. The data collected during randomized clinical trials are often used to estimate the optimal ITRs. However, these trials are generally expensive to run, and, moreover, they are not designed-to efficiently estimate ITRs. In this article, we propose a cost-effective estimation method from an active learning...
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作者：Zhou, Bo; Moorman, David E.; Behseta, Sam; Ombao, Hernando; Shahbaba, Babak
作者单位：University of California System; University of California Irvine
摘要：The goal of this article is to develop a novel statistical model for studying cross-neuronal spike train interactions during decision-making. For an individual to successfully complete the task of decision-making, a number of temporally organized events must occur: stimuli must be detected, potential outcomes must be evaluated, behaviors must be executed or inhibited, and outcomes (such as reward or no-reward) must be experienced. Due to the complexity of this process, it is likely the case th...
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作者：Bradley, Jonathan R.; Wikle, Christopher K.; Holan, Scott H.
作者单位：University of Missouri System; University of Missouri Columbia
摘要：We introduce Bayesian spatial change of support (COS) methodology for count-valued survey data with known survey variances. Our proposed methodology is motivated by the American Community Survey (ACS), an ongoing survey administered by the U.S. Census Bureau that provides timely information on several key demographic variables. Specifically, the ACS produces 1-year, 3-year, and 5-year period-estimates, and corresponding margins of errors, for published demographic and socio-economic variables ...
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作者：Porcu, Emilio; Bevilacqua, Moreno; Genton, Marc G.
作者单位：Universidad Tecnica Federico Santa Maria
摘要：In this article, we propose stationary covariance functions for processes that evolve temporally over a sphere, as well as cross-covariance functions for multivariate random fields defined over a sphere. For such processes, the great circle distance is the natural metric that should be used to describe spatial dependence. Given the mathematical difficulties for the construction of covariance functions for processes defined over spheres cross time, approximations of the state of nature have bee...
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作者：Yang, Yun; Dunson, David B.
作者单位：Duke University
摘要：In many application areas, data are collected on a categorical response and high-dimensional categorical predictors, with the goals being to build a parsimonious model for classification while doing inferences on the important predictors. In settings such as genomics, there can be complex interactions among the predictors. By using a carefully structured Tucker factorization, we define a model that can characterize any conditional probability, while facilitating variable selection and modeling...
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作者：Gaynanova, Irina; Booth, James G.; Wells, Martin T.
作者单位：Cornell University
摘要：This article considers the problem of sparse estimation of canonical vectors in linear discriminant analysis when p >> N. Several methods have been proposed in the literature that estimate one canonical vector in the two-group case. However, G 1 canonical vectors can be considered if the number of groups is G. In the multi-group context, it is common to estimate canonical vectors in a sequential fashion. Moreover, separate prior estimation of the covariance structure is often required. We prop...
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作者：Bunch, Pete; Godsill, Simon
作者单位：University of Cambridge
摘要：Recently developed particle flow algorithms provide an alternative to importance sampling for drawing particles from a posterior distribution, and a number of particle filters based on this principle have been proposed. Samples are drawn from the prior and then moved according to some dynamics over an interval of pseudo-time such that their final values are distributed according to the desired posterior. In practice, implementing a particle flow, sampler requires multiple layers of approximati...
