Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/12878
Title: Variational Bayesian Inference for Parametric and Nonparametric Regression With Missing Data
Authors: FAES, Christel 
Ormerod, J. T.
Wand, M. P.
Issue Date: 2011
Publisher: AMER STATISTICAL ASSOC
Source: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 106(495), p. 959-971
Abstract: Bayesian hierarchical models are attractive structures for conducting regression analyses when the data are subject to missingness. However, the requisite probability calculus is challenging and Monte Carlo methods typically are employed. We develop an alternative approach based on deterministic variational Bayes approximations. Both parametric and nonparametric regression are considered. Attention is restricted to the more challenging case of missing predictor data. We demonstrate that variational Bayes can achieve good accuracy, but with considerably less computational overhead. The main ramification is fast approximate Bayesian inference in parametric and nonparametric regression models with missing data. Supplemental materials accompany the online version of this article.
Notes: Faes, C (reprint author), Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, BE-3590 Diepenbeek, Belgium. [Ormerod, J. T.] Univ Sydney, Sch Math & Stat, Sydney, NSW 2006, Australia. [Wand, M. P.] Univ Technol Sydney, Sch Math Sci, Sydney, NSW 2007, Australia. matt.wand@uts.edu.au
Keywords: Statistics & Probability, semiparametric regression; modelling framework; graphical models; priors; approximations; distributions; binary;Directed acyclic graphs; Incomplete data; Mean field approximation; Penalized splines; Variational approximation
Document URI: http://hdl.handle.net/1942/12878
ISSN: 0162-1459
e-ISSN: 1537-274X
DOI: 10.1198/jasa.2011.tm10301
ISI #: 000296224200024
Category: A1
Type: Journal Contribution
Validations: ecoom 2012
Appears in Collections:Research publications

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