Parameter estimation for discrete distributions by generalized hellinger-type divergence based on probability generating function

S. Z. Sim; S. H. Ong

doi:10.1080/03610910903443980

Parameter estimation for discrete distributions by generalized hellinger-type divergence based on probability generating function

S. Z. Sim, S. H. Ong

Research output: Contribution to journal › Article › Research › peer-review

22 Citations (Scopus)

Abstract

This article considers a probability generating function-based divergence statistic for parameter estimation. The performance and robustness of the proposed statistic in parameter estimation is studied for the negative binomial distribution by Monte Carlo simulation, especially in comparison with the maximum likelihood and minimum Hellinger distance estimation. Numerical examples are given as illustration of goodness of fit.

Original language	English
Pages (from-to)	305-314
Number of pages	10
Journal	Communications in Statistics - Simulation and Computation
Volume	39
Issue number	2
DOIs	https://doi.org/10.1080/03610910903443980
Publication status	Published - Feb 2010
Externally published	Yes

Keywords

Maximum likelihood
Mean square error
Minimum Hellinger distance
Monte Carlo
Negative binomial distribution
Outliers

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10.1080/03610910903443980

Cite this

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abstract = "This article considers a probability generating function-based divergence statistic for parameter estimation. The performance and robustness of the proposed statistic in parameter estimation is studied for the negative binomial distribution by Monte Carlo simulation, especially in comparison with the maximum likelihood and minimum Hellinger distance estimation. Numerical examples are given as illustration of goodness of fit.",

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AB - This article considers a probability generating function-based divergence statistic for parameter estimation. The performance and robustness of the proposed statistic in parameter estimation is studied for the negative binomial distribution by Monte Carlo simulation, especially in comparison with the maximum likelihood and minimum Hellinger distance estimation. Numerical examples are given as illustration of goodness of fit.

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KW - Mean square error

KW - Minimum Hellinger distance

KW - Monte Carlo

KW - Negative binomial distribution

KW - Outliers

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Parameter estimation for discrete distributions by generalized hellinger-type divergence based on probability generating function

Abstract

Keywords

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