A novel weighted likelihood estimation with empirical Bayes flavor

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

Abstract

We propose a novel approach to estimation, where a set of estimators of a parameter is combined into a weighted average to produce the final estimator. The weights are chosen to be proportional to the likelihood evaluated at the estimators. We investigate the method for a set of estimators obtained by using the maximum likelihood principle applied to each individual observation. The method can be viewed as a Bayesian approach with a data-driven prior distribution. We provide several examples illustrating the new method and argue for its consistency, asymptotic normality, and efficiency. We also conduct simulation studies to assess the performance of the estimators. This straightforward methodology produces consistent estimators comparable with those obtained by the maximum likelihood method. The method also approximates the distribution of the estimator through the “posterior” distribution.

Detaljer

Författare
Enheter & grupper
Externa organisationer
  • University of Nevada, Reno
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Sannolikhetsteori och statistik

Nyckelord

Originalspråkengelska
Sidor (från-till)392-412
TidskriftCommunications in Statistics: Simulation and Computation
Volym47
Utgåva nummer2
Tidigt onlinedatum2017 dec 18
StatusPublished - 2018 feb 7
PublikationskategoriForskning
Peer review utfördJa

Relaterad forskningsoutput

Hossain, M., Kozubowski, T. & Krzysztof Podgorski, 2015, Department of Statistics, Lund university, 28 s. (Working Papers in Statistics; nr. 6).

Forskningsoutput: Working paper

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