Efficient closed-form maximum a posteriori estimators for the gamma distribution

We proposed a new class of maximum a posteriori estimators for the parameters of the Gamma distribution. These estimators have simple closed-form expressions and can be rewritten as a bias-corrected maximum likelihood estimators presented by Ye and Chen [Closed-form estimators for the gamma distribu...

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Bibliographic Details
Published in:Journal of statistical computation and simulation 2018-04, Vol.88 (6), p.1134-1146
Main Authors: Louzada, Francisco, Ramos, Pedro Luiz
Format: Article
Language:English
Subjects:
Bayesian analysis
Closed form solutions
closed-form estimator
Computer simulation
Economic models
Estimating techniques
Exact solutions
Gamma distribution
Maximum likelihood estimators
Parameter estimation
Probability distribution functions
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Summary:We proposed a new class of maximum a posteriori estimators for the parameters of the Gamma distribution. These estimators have simple closed-form expressions and can be rewritten as a bias-corrected maximum likelihood estimators presented by Ye and Chen [Closed-form estimators for the gamma distribution derived from likelihood equations. Am Statist. 2017;71(2):177-181]. A simulation study was carried out to compare different estimation procedures. Numerical results revels that our new estimation scheme outperforms the existing closed-form estimators and produces extremely efficient estimates for both parameters, even for small sample sizes.
ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2017.1422503