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作者：Lee, Seonjoo; Shen, Haipeng; Truong, Young; Lewis, Mechelle; Huang, Xuemei
作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Penn State Health; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Penn State Health
摘要：Independent component analysis (ICA) is an effective data-driven method for blind source separation. It has been successfully applied to separate source signals of interest from their mixtures. Most existing ICA procedures are carried out by relying solely on the estimation of the marginal density functions, either parametrically or nonparametrically. In many applications, correlation structures within each source also play an important role besides the marginal distributions. One important ex...
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作者：Ma, Yanyuan; Ronchetti, Elvezio
作者单位：Texas A&M University System; Texas A&M University College Station; University of Geneva; University of Geneva
摘要：We develop second-order hypothesis testing procedures in functional measurement error models for small or moderate sample sizes, where the classical first-order asymptotic analysis often fails to provide accurate results. In functional models no distributional assumptions are made on the unobservable covariates and this leads to semiparametric models. Our testing procedure is derived using saddlepoint techniques and is based on an empirical distribution estimation subject to the null hypothesi...
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作者：Cai, Tony; Liu, Weidong
作者单位：University of Pennsylvania; Shanghai Jiao Tong University; Shanghai Jiao Tong University
摘要：This article considers sparse linear discriminant analysis of high-dimensional data. In contrast to the existing methods which are based on separate estimation of the precision matrix Omega and the difference delta of the mean vectors, we introduce a simple and effective classifier by estimating the product Omega delta directly through constrained l(1) minimization. The estimator can be implemented efficiently using linear programming and the resulting classifier is called the linear programmi...
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作者：Frees, Edward W.; Meyers, Glenn; Cummings, A. David
作者单位：University of Wisconsin System; University of Wisconsin Madison
摘要：Individuals, corporations and government entities regularly exchange financial risks y at prices Pi. Comparing distributions of risks and prices can be difficult, particularly when the financial risk distribution is complex. For example, with insurance, it is not uncommon for a risk distribution to be a mixture of 0's (corresponding to no claims) and a right-skewed distribution with thick tails (the claims distribution). However, analysts do not work in a vacuum, and in the case of insurance t...
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作者：Alimadad, Azadeh; Salibian-Barrera, Matias
作者单位：Simon Fraser University; Simon Fraser University; University of British Columbia
摘要：We are interested in a class of unsupervised methods to detect possible disease outbreaks, that is, rapid increases in the number of cases of a particular disease that deviate from the pattern observed in the past. The motivating application for this article deals with detecting outbreaks using generalized additive models (GAMs) to model weekly counts of certain infectious diseases. We can use the distance between the predicted and observed counts for a specific week to determine whether an im...
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作者：Bergesio, Andrea; Yohai, Victor J.
作者单位：Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); National University of the Littoral; University of Buenos Aires
摘要：We introduce a new class of robust estimators for generalized linear models which is an extension of the class of projection estimators for linear regression. These projection estimators are defined using an initial robust estimator for a generalized linear model with only one unknown parameter. We found a bound for the maximum asymptotic bias of the projection estimator caused by a fraction epsilon of outlier contamination. For small epsilon, this bias is approximately twice the maximum bias ...
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作者：Paige, Robert L.; Chapman, Phillip L.; Butler, Ronald W.
作者单位：University of Missouri System; Missouri University of Science & Technology; Colorado State University System; Colorado State University Fort Collins; Southern Methodist University
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作者：Ruth, David M.; Koyak, Robert A.
作者单位：United States Department of Defense; United States Navy; United States Naval Academy; United States Department of Defense; United States Navy; Naval Postgraduate School
摘要：Given a sequence of observations, has a change occurred in the underlying probability distribution with respect to observation order? This problem of detecting change points arises in a variety of applications including health prognostics for mechanical systems, syndromic disease surveillance in geographically dispersed populations, anomaly detection in information networks, and multivariate process control in general. Detecting change points in high-dimensional settings is challenging, and mo...
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作者：Jiang, Jiming; Thuan Nguyen; Rao, J. Sunil
作者单位：University of California System; University of California Davis; Oregon Health & Science University; University of Miami
摘要：We derive the best predictive estimator (BPE) of the fixed parameters under two well-known small area models, the Fay-Herriot model and the nested-error regression model. This leads to a new prediction procedure, called observed best prediction (OBP), which is different from the empirical best linear unbiased prediction (EBLUP). We show that BPE is more reasonable than the traditional estimators derived from estimation considerations, such as maximum likelihood (ML) and restricted maximum like...
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作者：Culp, Mark
作者单位：West Virginia University
摘要：This article presents a semisupervised modeling framework that combines feature-based (x) data and graph-based (G) data for classification/regression of the response Y. In this semisupervised setting, Y is observed for a subset of the observations (labeled) and missing for the remainder (unlabeled). The Propagated Scoring algorithm proposed for fitting this model is a semisupervised fixed-point regularization approach that essentially extends the generalized additive model into the semisupervi...
