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作者：Rosen, Ori; Wood, Sally; Stoffer, David S.
作者单位：University of Texas System; University of Texas El Paso; University of Melbourne; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
摘要：We propose a method for analyzing possibly nonstationary time series by adaptively dividing the time series into an unknown but finite number of segments and estimating the corresponding Meal spectra by smoothing splines. The model is formulated in a Bayesian framework, and the estimation relies on reversible jump Markov chain Monte Carlo (RJMCMC) methods. For a given segmentation of the time,series, the likelihood function is approximated via a product of local Whittle likelihoods. Thus, no p...
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作者：Han, Summer S.; Rosenberg, Philip S.; Chatterjee, Nilanjan
作者单位：National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics
摘要：Recent genome-wide association studies (GWASs) designed to detect the main effects of genetic markers have had considerable success with many findings validated by replication studies. However, relatively few findings of gene-gene or gene-environment interactions have been successfully reproduced. Besides the main issues associated with insufficient sample size in current studies, a complication is that interactions that rank high based on p-values often correspond to extreme forms of joint ef...
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作者：Zubizarreta, Jose R.
作者单位：University of Pennsylvania
摘要：This article presents a new method for optimal matching in observational studies based on mixed integer programming. Unlike widely used matching methods based on network algorithms, which attempt to achieve covariate balance by minimizing the total sum of distances between treated units and matched controls, this new method achieves covariate balance directly, either by minimizing both the total sum of distances and a weighted sum of specific measures of covariate imbalance, or by minimizing t...
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作者：Lindquist, Martin A.
作者单位：Columbia University
摘要：Mediation analysis is often used in the behavioral sciences to investigate the role of intermediate variables that lie on the causal path between a randomized treatment and an outcome variable. Typically, mediation is assessed using structural equation models (SEMs), with model coefficients interpreted as causal effects. In this article, we present an extension of SEMs to the functional data analysis (FDA) setting that allows the mediating variable to be a continuous function rather than a sin...
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作者：Scheipl, Fabian; Fahrmeir, Ludwig; Kneib, Thomas
作者单位：University of Munich; University of Gottingen
摘要：Structured additive regression (STAR) provides a general framework for complex Gaussian and non-Gaussian regression models, with predictors comprising arbitrary combinations of nonlinear functions and surfaces, spatial effects, varying coefficients, random effects, and further regression terms. The large flexibility, of STAR makes function selection a challenging and important task, aiming at (1) selecting the relevant covariates, (2) choosing an appropriate and parsimonious representation of ...
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作者：Telesca, Donatello; Erosheva, Elena A.; Kreager, Derek A.; Matsueda, Ross L.
作者单位：University of California System; University of California Los Angeles; University of Washington; University of Washington Seattle; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; University of Washington; University of Washington Seattle
摘要：A major aim of longitudinal analyses of life-course data is to describe the within- and between-individual variability in a behavioral outcome, such as crime. Statistical analyses of such data typically draw on mixture and mixed-effects growth models. In this work, we present a functional analytic point of view and develop an alternative method that models individual crime trajectories as departures from a population age crime curve. Drawing on empirical and theoretical claims in criminology, ...
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作者：Xing, Haipeng; Ying, Zhiliang
作者单位：State University of New York (SUNY) System; Stony Brook University; Columbia University
摘要：Many longitudinal studies involve relating an outcome process to a set of possibly time-varying covariates, giving rise to the usual regression models for longitudinal data. When the purpose of the study is to investigate the covariate effects when experimental environment undergoes abrupt changes or to locate the periods with different levels of covariate effects, a simple and easy-to-interpret approach is to introduce change-points in regression coefficients. In this connection, we propose a...
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作者：Chen, Lisha; Huang, Jianhua Z.
作者单位：Yale University; Texas A&M University System; Texas A&M University College Station
摘要：The reduced-rank regression is an effective method in predicting multiple response variables from the same set of predictor variables. It reduces the dumber of model parameters and takes advantage of interrelations between. the response variables and hence improves predictive accuracy. We propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty. We apply a group-lasso type penalty that treats each row of the matrix of the regression coefficients as ...
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作者：Wiesenfarth, Manuel; Krivobokova, Tatyana; Klasen, Stephan; Sperlich, Stefan
作者单位：University of Gottingen; University of Gottingen; University of Gottingen; University of Gottingen; University of Geneva
摘要：This article proposes a simple and fast approach to build simultaneous confidence bands and perform specification tests for smooth curves in additive models. The method allows for handling of spatially heterogeneous functions and its derivatives as well as heteroscedasticity in the data. It is applied to study the determinants of chronic undernutrition of Kenyan children, with a particular focus on the highly nonlinear age pattern in undernutrition. Model estimation using the mixed model repre...
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作者：Bondell, Howard D.; Reich, Brian J.
作者单位：North Carolina State University
摘要：For high-dimensional data, particularly when the number of predictors greatly exceeds the sample size, selection of relevant predictors for regression is a challenging problem. Methods such as sure screening, forward selection, or penalized regressions are commonly used. Bayesian variable selection methods place prior distributions on the parameters along with a prior over model space, or equivalently, a mixture prior on the parameters having mass at zero. Since exhaustive enumeration is not f...
