Statistical inference based on a new weighted likelihood approach

We discuss a new weighted likelihood method for robust parametric estimation. The method is motivated by the need for generating a simple estimation strategy which provides a robust solution that is simultaneously fully efficient when the model is correctly specified. This is achieved by appropriate...

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Vydané v:Metrika Ročník 84; číslo 1; s. 97 - 120
Hlavní autori: Majumder, Suman, Biswas, Adhidev, Roy, Tania, Bhandari, Subir Kumar, Basu, Ayanendranath
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2021
Springer Nature B.V
Predmet:
Economic Theory/Quantitative Economics/Mathematical Methods
Estimators
Mathematics and Statistics
Parameter estimation
Probability Theory and Stochastic Processes
Robustness (mathematics)
Statistical inference
Statistics
Weighting functions
Influence function
Robustness
Robust regression
Weighted likelihood
Asymptotic efficiency
ISSN:0026-1335, 1435-926X
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Shrnutí:We discuss a new weighted likelihood method for robust parametric estimation. The method is motivated by the need for generating a simple estimation strategy which provides a robust solution that is simultaneously fully efficient when the model is correctly specified. This is achieved by appropriately weighting the score function at each observation in the maximum likelihood score equation. The weight function determines the compatibility of each observation with the model in relation to the remaining observations and applies a downweighting only if it is necessary, rather than automatically downweighting a proportion of the observations all the time. This allows the estimators to retain full asymptotic efficiency at the model. We establish all the theoretical properties of the proposed estimators and substantiate the theory developed through simulation and real data examples. Our approach provides an alternative to the weighted likelihood method of Markatou et al. (J Stat Plan Inference 57(2):215–232, 1997; J Am Stat Assoc 93(442):740–750, 1998).
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0026-1335
1435-926X
DOI:10.1007/s00184-020-00778-y