Estimating parameters for discrete distributions via the empirical probability generating function

DOI10.1080/03610919708813381zbMath0902.62032OpenAlexW2067653203MaRDI QIDQ4387662

Mary M. Dowling, Miguel Nakamura

Publication date: 25 November 1998

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

Full work available at URL: https://doi.org/10.1080/03610919708813381

zbMATH Keywords

m-estimatorsdistributions for counts

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12)

Related Items (6)

Parameter estimation by minimizing a probability generating function-based power divergence ⋮ Minimum distance estimators for count data based on the probability generating function with applications ⋮ The tempered discrete Linnik distribution ⋮ Empirical probability generating function. An overview ⋮ Parameter estimation by Hellinger type distance for multivariate distributions based upon probability generating functions ⋮ Probability Generating Function Based Jeffrey's Divergence for Statistical Inference

Cites Work

• A goodness of fit test for the Poisson distribution based on the empirical generating function
• Goodness of fit for the poisson distribution based on the probability generating function
• Empirical Laplace transform and approximation of compound distributions
• On statistical transform methods and their efficiency
• Use of an empirical probability generating function for testing a Poisson model
• Exploratory data analysis for counts using the empirical probability generating function¹
• Goodness of fit tests for discrete distributions
• A Rapid and Efficient Estimation Procedure for the Negative Binomial Distribution

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