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| 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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Page views:: 786. Submitted: 2017-11-30. Published: 2020-09-02. |
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| Paper: |
deconvolveR: A G-Modeling Program for Deconvolution and Empirical Bayes Estimation
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| DOI: |
10.18637/jss.v094.i11
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This work is licensed under the licenses Paper: Creative Commons Attribution 3.0 Unported License Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license. |