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bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors

Authors
Jo, SeongilChoi, TaeryonPark, BeomjoLenk, Peter
Issue Date
7월-2019
Publisher
JOURNAL STATISTICAL SOFTWARE
Keywords
cosine basis; Gaussian process priors; Markov chain Monte Carlo; R; shape restrictions; semiparametric models; spectral representation
Citation
JOURNAL OF STATISTICAL SOFTWARE, v.90, no.10
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF STATISTICAL SOFTWARE
Volume
90
Number
10
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/64250
DOI
10.18637/jss.v090.i10
ISSN
1548-7660
Abstract
The Bayesian spectral analysis model (BSAM) is a powerful tool to deal with semiparametric methods in regression and density estimation based on the spectral representation of Gaussian process priors. The bsamGP package for R provides a comprehensive set of programs for the implementation of fully Bayesian semiparametric methods based on BSAM. Currently, bsamGP includes semiparametric additive models for regression, generalized models and density estimation. In particular, bsamGP deals with constrained regression models with monotone, convex/concave, S-shaped and U-shaped functions by modeling derivatives of regression functions as squared Gaussian processes. bsamGP also contains Bayesian model selection procedures for testing the adequacy of a parametric model relative to a non-specific semiparametric alternative and the existence of the shape restriction. To maximize computational efficiency, we carry out posterior sampling algorithms of all models using compiled Fortran code. The package is illustrated through Bayesian semiparametric analyses of synthetic data and benchmark data.
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정경대학 (통계학과)
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