Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Balasubramanian Narasimhan, Bradley Efron
Title: deconvolveR: A G-Modeling Program for Deconvolution and Empirical Bayes Estimation
Abstract: Empirical Bayes inference assumes an unknown prior density g(θ) has yielded (unobservables) Θ1, Θ2, ..., ΘN, and each Θi produces an independent observation Xi from pi (Xi | Θi). The marginal density fi (Xi) is a convolution of the prior g and pi. The Bayes deconvolution problem is one of recovering g from the data. Although estimation of g - so called g-modeling - is difficult, the results are more encouraging if the prior g is restricted to lie within a parametric family of distributions. We present a deconvolution approach where g is restricted to be in a parametric exponential family, along with an R package deconvolveR designed for the purpose.

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Paper: deconvolveR: A G-Modeling Program for Deconvolution and Empirical Bayes Estimation     Download PDF (Downloads: 225)
Supplements:
deconvolveR_1.2-1.tar.gz: R source package Download (Downloads: 12; 1MB)
v94i11.R: R replication code Download (Downloads: 16; 17KB)

DOI: 10.18637/jss.v094.i11

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Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.