A comparison of related density‐based minimum divergence estimators

This paper compares the minimum divergence estimator of Basu et al. (1998) to a competing minimum divergence estimator which turns out to be equivalent to a method proposed from a different perspective by Windham (1995). Both methods can be applied for any parametric model and contain maximum likeli...

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Bibliographic Details
Published in:Biometrika 2001-10, Vol.88 (3), p.865-873
Main Authors: Jones, M. C., Hjort, Nils Lid, Harris, Ian R., Basu, Ayanendranath
Format: Article
Language:English
Subjects:
Asymptotic relative efficiency
Density
Density estimation
Divergence
Efficiency
Efficiency loss
Estimation methods
Estimators
Euclidean space
Exact sciences and technology
Expected values
Influence function
Mathematical foundations
Mathematical minima
Mathematical robustness
Mathematics
Maximum likelihood
Maximum likelihood estimation
Maximum likelihood estimators
Miscellanea
M‐estimation
Parametric models
Probability and statistics
Robustness
Sciences and techniques of general use
Statistics
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Summary:This paper compares the minimum divergence estimator of Basu et al. (1998) to a competing minimum divergence estimator which turns out to be equivalent to a method proposed from a different perspective by Windham (1995). Both methods can be applied for any parametric model and contain maximum likelihood as a special case. Efficiencies are compared under model conditions, and robustness properties are studied. Overall the two methods are found to perform quite similarly. Some relatively small advantages of the former method over the latter are identified.
ISSN:0006-3444
1464-3510
DOI:10.1093/biomet/88.3.865