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2017 A variational Bayes approach to a semiparametric regression using Gaussian process priors
Victor M. H. Ong, David K. Mensah, David J. Nott, Seongil Jo, Beomjo Park, Taeryon Choi
Electron. J. Statist. 11(2): 4258-4296 (2017). DOI: 10.1214/17-EJS1324


This paper presents a variational Bayes approach to a semiparametric regression model that consists of parametric and nonparametric components. The assumed univariate nonparametric component is represented with a cosine series based on a spectral analysis of Gaussian process priors. Here, we develop fast variational methods for fitting the semiparametric regression model that reduce the computation time by an order of magnitude over Markov chain Monte Carlo methods. Further, we explore the possible use of the variational lower bound and variational information criteria for model choice of a parametric regression model against a semiparametric alternative. In addition, variational methods are developed for estimating univariate shape-restricted regression functions that are monotonic, monotonic convex or monotonic concave. Since these variational methods are approximate, we explore some of the trade-offs involved in using them in terms of speed, accuracy and automation of the implementation in comparison with Markov chain Monte Carlo methods and discuss their potential and limitations.


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Victor M. H. Ong. David K. Mensah. David J. Nott. Seongil Jo. Beomjo Park. Taeryon Choi. "A variational Bayes approach to a semiparametric regression using Gaussian process priors." Electron. J. Statist. 11 (2) 4258 - 4296, 2017.


Received: 1 August 2016; Published: 2017
First available in Project Euclid: 8 November 2017

zbMATH: 06805092
MathSciNet: MR3720915
Digital Object Identifier: 10.1214/17-EJS1324

Primary: 62G08
Secondary: 62F15

Keywords: cosine series , Gaussian process , Model selection , shape restricted regression , variational Bayes


Vol.11 • No. 2 • 2017
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