Parameter Estimation for Discrete Distributions by Generalized Hellinger-Type Divergence Based on Probability Generating Function

DOI10.1080/03610910903443980zbMath1184.62027OpenAlexW1482672413MaRDI QIDQ5305509

Publication date: 22 March 2010

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610910903443980

zbMATH Keywords

outliers robustness Monte Carlo maximum likelihood negative binomial distribution mean square error minimum Hellinger distance

Mathematics Subject Classification ID

Point estimation (62F10) Monte Carlo methods (65C05) Characteristic functions; other transforms (60E10)

Related Items (13)

Goodness-of-fit test for count distributions with finite second moment ⋮ Analysis of discrete data by Conway-Maxwell Poisson distribution ⋮ Statistical Analysis for the Inverse Trinomial Distribution ⋮ Parameter estimation by minimizing a probability generating function-based power divergence ⋮ On some properties and applications of intervened cluster negative binomial distribution ⋮ Minimum distance estimators for count data based on the probability generating function with applications ⋮ Unnamed Item ⋮ A generalized inverse trinomial distribution with application ⋮ A class of tests for the two-sample problem for count data ⋮ A Bivariate Generalization of the Noncentral Negative Binomial Distribution ⋮ Parameter estimation by Hellinger type distance for multivariate distributions based upon probability generating functions ⋮ Probability Generating Function Based Jeffrey's Divergence for Statistical Inference ⋮ A model selection criterion for count models based on a divergence between probability generating functions

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