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作者：Stefanski, L. A.; Wu, Yichao; White, Kyle
作者单位：North Carolina State University
摘要：Using the relationships among ridge regression, LASSO estimation, and measurement error attenuation as motivation, a new measurement-error-model-based approach to variable selection is developed. After describing the approach in the familiar context of linear regression, we apply it to the problem of variable selection in nonparametric classification, resulting in a new kernel-based classifier with LASSO-like shrinkage and variable-selection properties. Finite-sample performance of the new cla...
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作者：Schmertmann, Carl; Zagheni, Emilio; Goldstein, Joshua R.; Myrskylae, Mikko
作者单位：State University System of Florida; Florida State University; City University of New York (CUNY) System; Queens College NY (CUNY); University of California System; University of California Berkeley; University of London; London School Economics & Political Science
摘要：There are signs that fertility in rich countries may have stopped declining, but this depends critically on whether women currently in reproductive ages are postponing or reducing lifetime fertility. Analysis of average completed family sizes requires forecasts of remaining fertility for women born 1970-1995. We propose a Bayesian model for fertility that incorporates a priori information about patterns over age and time. We use a new dataset, the Human Fertility Database (HFD), to construct i...
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作者：Zhou, Hua; Wu, Yichao
作者单位：North Carolina State University
摘要：Regularization is widely used in statistics and machine learning to prevent overfitting and gear solution toward prior information. In general, a regularized estimation problem minimizes the sum of a loss function and a penalty term. The penalty term is usually weighted by a tuning parameter and encourages certain constraints on the parameters to be estimated. Particular choices of constraints lead to the popular lasso, fused-lasso, and other generalized l(1) penalized regression methods. In t...
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作者：Scarpa, Bruno; Dunson, David B.
作者单位：University of Padua; Duke University
摘要：In many applications involving functional data, prior information is available about the proportion of curves having different attributes. It is not straightforward to include such information in existing procedures for functional data analysis. Generalizing the functional Dirichlet process (FDP), we propose a class of stick-breaking priors for distributions of functions. These priors incorporate functional atoms drawn from constrained stochastic processes. The stick-breaking weights are speci...
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作者：Bar, Haim Y.; Booth, James G.; Wells, Martin T.
作者单位：University of Connecticut; Cornell University; Cornell University
摘要：We develop a novel approach for testing treatment effects in high-throughput data. Most previous works on this topic focused on testing for differences between the means, but recently it has been recognized that testing for differential variation is probably as important. We take it a step further, and introduce a bivariate model modeling strategy which accounts for both differential expression and differential variation. Our model-based approach, in which the differential mean and variance ar...
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作者：Pan, Guangming; Gao, Jiti; Yang, Yanrong
作者单位：Nanyang Technological University; Monash University
摘要：random vectors of length p in the form of a matrix, we develop a linear spectral statistic of the constructed matrix to test whether the n random vectors are independent or not. Specifically, the proposed statistic can also be applied to n random vectors, each of whose elements can be written as either a linear stationary process or a linear combination of independent random variables. The asymptotic distribution of the proposed test statistic is established for the case of 0 < lim(n ->infinit...
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作者：Antoniano-Villalobos, Isadora; Wade, Sara; Walker, Stephen G.
作者单位：Bocconi University; University of Cambridge; University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin
摘要：Hippocampal volume is one of the best established biomarkers for Alzheimer's disease. However, for appropriate use in clinical trials research, the evolution of hippocampal volume needs to be well understood. Recent theoretical models propose a sigmoidal pattern for its evolution. To support this theory, the use of Bayesian nonparametric regression mixture models seems particularly suitable due to the flexibility that models of this type can achieve and the unsatisfactory predictive properties...
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作者：Lei, Jing
作者单位：Carnegie Mellon University
摘要：This article studies global testing of the slope function in functional linear regression models. A major challenge in functional global testing is to choose the dimension of projection when approximating the functional regression model by a finite dimensional multivariate linear regression model. We develop a new method that simultaneously tests the slope vectors in a sequence of functional principal components regression models. The sequence of models being tested is determined by the sample...
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作者：Lijoi, Antonio; Nipoti, Bernardo
作者单位：University of Pavia; University of Turin; Collegio Carlo Alberto
摘要：Mixture models for hazard rate functions are widely used tools for addressing the statistical analysis of survival data subject to a censoring mechanism. The present article introduced a new class of vectors of random hazard rate functions that are expressed as kernel mixtures of dependent completely random measures. This leads to define dependent nonparametric prior processes that are suitably tailored to draw inferences in the presence of heterogenous observations. Besides its flexibility, a...
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作者：Liang, Faming; Cheng, Yichen; Lin, Guang
作者单位：Texas A&M University System; Texas A&M University College Station; United States Department of Energy (DOE); Pacific Northwest National Laboratory
摘要：Simulated annealing has been widely used in the solution of optimization problems. As known by many researchers, the global optima cannot be guaranteed to be located by simulated annealing unless a logarithmic cooling schedule is used. However, the logarithmic cooling schedule is so slow that no one can afford to use this much CPU time. This article proposes a new stochastic optimization algorithm, the so-called simulated stochastic approximation annealing algorithm, which is a combination of ...
