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作者：Womack, Andrew J.; Leon-Novelo, Luis; Casella, George
作者单位：Indiana University System; Indiana University Bloomington; State University System of Florida; University of Florida; University of Louisiana Lafayette
摘要：In this article, we present a fully coherent and consistent objective Bayesian analysis of the linear regression model using intrinsic priors. The intrinsic prior is a scaled mixture of g-priors and promotes shrinkage toward the subspace defined by a base (or null) model. While it has been established that the intrinsic prior provides consistent model selectors across a range of models, the posterior distribution of the model parameters has not previously been investigated. We prove that the p...
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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：Kundu, Suprateek; Dunson, David B.
作者单位：Texas A&M University System; Texas A&M University College Station; Duke University
摘要：There is a rich literature on Bayesian variable selection for parametric models. Our focus is on generalizing methods and asymptotic theory established for mixtures of g-priors to semiparametric linear regression models having unknown residual densities. Using a Dirichlet process location mixture for the residual density, we propose a semiparametric g-prior which incorporates an unknown matrix of cluster allocation indicators. For this class of priors, posterior computation can proceed via a s...
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作者：Wierzbicki, Michael R.; Guo, Li-Bing; Du, Qing-Tao; Guo, Wensheng
作者单位：University of Pennsylvania; Guangdong Pharmaceutical University
摘要：Traditional Chinese herbal medications (TCHMs) are composed of a multitude of compounds and the identification of their active composition is an important area of research. Chromatography provides a visual representation of a TCHM sample's composition by outputting a curve characterized by spikes corresponding to compounds in the sample. Across different experimental conditions, the location of the spikes can be shifted, preventing direct comparison of curves and forcing compound identificatio...
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作者：Chen, Huaihou; Zeng, Donglin
作者单位：State University System of Florida; University of Florida; University of North Carolina; University of North Carolina Chapel Hill
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作者：Froelich, Markus; Huber, Martin
作者单位：University of Mannheim; IZA Institute Labor Economics; University of St Gallen
摘要：This article develops a nonparametric methodology for treatment evaluation with multiple outcome periods under treatment endogeneity and missing outcomes. We use instrumental variables, pretreatment characteristics, and short-term (or intermediate) outcomes to identify the average treatment effect on the outcomes of compliers (the subpopulation whose treatment reacts on the instrument) in multiple periods based on inverse probability weighting. Treatment selection and attrition may depend on b...
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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：Barthelme, Simon; Chopin, Nicolas
作者单位：University of Geneva; Institut Polytechnique de Paris; ENSAE Paris
摘要：Many models of interest in the natural and social sciences have no closed-form likelihood function, which means that they cannot be treated using the usual techniques of statistical inference. In the case where such models can be efficiently simulated, Bayesian inference is still possible thanks to the approximate Bayesian computation (ABC) algorithm. Although many refinements have been suggested, ABC inference is still far from routine. ABC is often excruciatingly slow due to very low accepta...
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作者：Fukumizu, Kenji; Leng, Chenlei
作者单位：Research Organization of Information & Systems (ROIS); Institute of Statistical Mathematics (ISM) - Japan; University of Warwick; National University of Singapore
摘要：This article proposes a novel approach to linear dimension reduction for regression using nonparametric estimation with positive-definite kernels or reproducing kernel Hilbert spaces (RKHSs). The purpose of the dimension reduction is to find such directions in the explanatory variables that explain the response sufficiently: this is called sufficient dimension reduction. The proposed method is based on an estimator for the gradient of the regression function considered for the feature vectors ...
