Asymptotic Moments Matching to Uniformly Minimum Variance Unbiased Estimation under Ewens Sampling Formula
Masayo Y. Hirose, Shuhei Mano
TL;DR
This paper derives asymptotic moments matching estimators for parameters under the Ewens sampling formula, addressing non-existence issues and evaluating their efficiency through simulations.
Contribution
It introduces a novel asymptotic moments matching approach for unbiased estimation under Ewens sampling, overcoming non-existence problems.
Findings
Derived first and second moments matching estimators
Evaluated estimator efficiency via Monte Carlo simulations
Provided insights into estimator performance under Ewens sampling
Abstract
The Ewens sampling formula is a distribution related to the random partition of a positive integer. In this study, we investigate the issue of non-existence solutions in parameter estimation under the distribution. As a result, the first and second moments matching estimators to the uniformly minimum variance unbiased estimator are derived using the Ewens sampling formula in asymptotic sense. A Monte Carlo simulation study is performed to evaluate the efficiency of the resulting estimators.
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Taxonomy
TopicsBayesian Methods and Mixture Models · Statistical Methods and Inference · Statistical Distribution Estimation and Applications