Affiliation:
1. Department of Statistics The Ohio State University Columbus Ohio USA
2. Department of Statistics and Probability Michigan State University East Lansing Michigan USA
3. Department of Applied Statistics Konkuk University Seoul Korea
Abstract
AbstractMaximum likelihood estimator (MLE) of the Dirichlet distribution is usually obtained by using the Newton–Raphson algorithm. However, in some cases, the computational costs can be burdensome, for example, in real‐time processes. Therefore, it is beneficial to develop a closed‐form estimator that is as efficient as the MLE for large sample. Here, we suggest asymptotically efficient closed‐form estimator based on the classical large sample theory.
Funder
National Research Foundation of Korea
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
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