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

DOI10.1080/03610910903443980zbMath1184.62027OpenAlexW1482672413MaRDI QIDQ5305509

Shin Zhu Sim, Seng Huat Ong

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

outliersrobustnessMonte Carlomaximum likelihoodnegative binomial distributionmean square errorminimum Hellinger distance

Mathematics Subject Classification ID

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

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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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