What is NPEB?

NPEB is a Non-Parametric Empirical Bayes estimation framework for compound estimation in the discrete linear exponential family, which includes a wide class of discrete distributions frequently arising from modern big data applications. The proposed framework directly estimates the Bayes shrinkage factor in the generalized Robbins’ formula via solving a scalable convex program, which is carefully developed based on a RKHS representation of the Stein’s discrepancy measure. The new NEB estimation framework is flexible for incorporating various structural constraints into the data driven rule, and provides a unified approach to compound estimation with both regular and scaled squared error losses.

How to use this repository? LINK to github repository

This repository holds the scripts that reproduce the analysis in the paper [1]. Send me an email if anything does not work as expected. If you are looking for the associated R-package npeb, please visit this page.

References

[1.] A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family (under review)
Banerjee, T., Liu, Q., Mukherjee, G. and Sun, W.