Functional regression approximate Bayesian computation for Gaussian process density estimation

G.S. Rodrigues, David J. Nott and S.A. Sisson

Computational Statistics & Data Analysis, 2016, vol. 103, issue C, 229-241

Abstract: A novel Bayesian nonparametric method is proposed for hierarchical modelling on a set of related density functions, where grouped data in the form of samples from each density function are available. Borrowing strength across the groups is a major challenge in this context. To address this problem, a hierarchically structured prior, defined over a set of univariate density functions using convenient transformations of Gaussian processes, is introduced. Inference is performed through approximate Bayesian computation (ABC) via a novel functional regression adjustment. The performance of the proposed method is illustrated via simulation studies and an analysis of rural high school exam performance in Brazil.

Keywords: Approximate Bayesian computation; Nonparametric density estimation; Gaussian process prior; Hierarchical models (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:103:y:2016:i:c:p:229-241

DOI: 10.1016/j.csda.2016.05.009

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