Composition Estimation via Shrinkage
Methodology
2020-05-29 v1
Abstract
In this note, we explore a simple approach to composition estimation, using penalized likelihood density estimation on a nominal discrete domain. Practical issues such as smoothing parameter selection and the use of prior information are investigated in simulations, and a theoretical analysis is attempted. The method has been implemented in a pair of R functions for use by practitioners.
Cite
@article{arxiv.2005.13988,
title = {Composition Estimation via Shrinkage},
author = {Chong Gu},
journal= {arXiv preprint arXiv:2005.13988},
year = {2020}
}