A g-Modeling Program for Deconvolution and Empirical Bayes Estimation
Empirical Bayes
deconvolution
Empirical Bayes inference assumes an unknown prior density g has yielded unobservable parameters, each producing an independent observation. The core challenge involves recovering the prior distribution from observed data through deconvolution. We propose restricting the prior to a parametric exponential family to make estimation tractable, and provide the R package deconvolveR implementing practical Bayes deconvolution and g-modeling techniques.