Krzysztof Podgórski
Professor, Head of the Department of Statistics
A novel weighted likelihood estimation with empirical Bayes flavor
Author
Summary, in English
We propose a novel approach to estimation, where a set of estimators of a parameter is combined into a weighted average to produce the final estimator. The weights are chosen to be proportional to the likelihood evaluated at the estimators. We investigate the method for a set of estimators obtained by using the maximum likelihood principle applied to each individual observation. The method can be viewed as a Bayesian approach with a data-driven prior distribution. We provide several examples illustrating the new method and argue for its consistency, asymptotic normality, and efficiency. We also conduct simulation studies to assess the performance of the estimators. This straightforward methodology produces consistent estimators comparable with those obtained by the maximum likelihood method. The method also approximates the distribution of the estimator through the “posterior” distribution.
Department/s
- Department of Statistics
Publishing year
2018-02-07
Language
English
Pages
392-412
Publication/Series
Communications in Statistics: Simulation and Computation
Volume
47
Issue
2
Document type
Journal article
Publisher
Taylor & Francis
Topic
- Probability Theory and Statistics
Keywords
- Consistency
- Data-dependent prior
- Empirical Bayes
- Exponentiated distribution
- Maximum likelihood estimator
- Super-efficiency
- Unbounded likelihood
Status
Published
ISBN/ISSN/Other
- ISSN: 0361-0918