deconvolveR: A G-Modeling Program for Deconvolution and Empirical Bayes Estimation
Dublin Core
Title
deconvolveR: A G-Modeling Program for Deconvolution and Empirical Bayes Estimation
Subject
Bayes deconvolution, g-modeling, empirical Bayes, missing species, R package deconvolveR
Description
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
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
Creator
Balasubramanian Narasimhan
Source
https://www.jstatsoft.org/article/view/v094i11
Publisher
Stanford University
Date
June 2020
Contributor
Fajar bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
Balasubramanian Narasimhan, “deconvolveR: A G-Modeling Program for Deconvolution and Empirical Bayes Estimation,” Repository Horizon University Indonesia, accessed May 14, 2025, https://repository.horizon.ac.id/items/show/8144.