Modified power divergence estimators in normal models - simulation and comparative study

Title:

Modified power divergence estimators in normal models - simulation and comparative study

Creator:

Frýdlová, Eva, Vajda, Igor, and Kůs, Václav

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:9cd8bddf-4c78-4efb-ad8a-f8876efa796b
uuid:9cd8bddf-4c78-4efb-ad8a-f8876efa796b

Subject:

minimum ϕ-divergence estimation, subdivergence, superdivergence, PC simulation, relative efficiency, and robustness

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Point estimators based on minimization of information-theoretic divergences between empirical and hypothetical distribution induce a problem when working with continuous families which are measure-theoretically orthogonal with the family of empirical distributions. In this case, the ϕ-divergence is always equal to its upper bound, and the minimum ϕ-divergence estimates are trivial. Broniatowski and Vajda \cite{IV09} proposed several modifications of the minimum divergence rule to provide a solution to the above mentioned problem. We examine these new estimation methods with respect to consistency, robustness and efficiency through an extended simulation study. We focus on the well-known family of power divergences parametrized by α∈R in the Gaussian model, and we perform a comparative computer simulation for several randomly selected contaminated and uncontaminated data sets, different sample sizes and different ϕ-divergence parameters.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Coverage:

[795]-808

Source:

Kybernetika | 2012 Volume:48 | Number:4

Harvested from:

CDK

Metadata only:

false