Composition Estimation via Shrinkage

05/28/2020

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by   Chong Gu, et al.

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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.