2020/05/28 by Gu, Chong
#FOS: Computer and information sciences #Methodology (stat.ME)
paper · doi:10.48550/arxiv.2005.13988
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.