Dual divergence estimators and tests: Robustness results

The class of dual ϕ -divergence estimators (introduced in Broniatowski and Keziou (2009)  [5]) is explored with respect to robustness through the influence function approach. For scale and location models, this class is investigated in terms of robustness and asymptotic relative efficiency. Some hyp...

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Veröffentlicht in:Journal of multivariate analysis 2011-01, Vol.102 (1), p.20-36
Hauptverfasser: Toma, Aida, Broniatowski, Michel
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container_title Journal of multivariate analysis
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creator Toma, Aida
Broniatowski, Michel
description The class of dual ϕ -divergence estimators (introduced in Broniatowski and Keziou (2009)  [5]) is explored with respect to robustness through the influence function approach. For scale and location models, this class is investigated in terms of robustness and asymptotic relative efficiency. Some hypothesis tests based on dual divergence criteria are proposed and their robustness properties are studied. The empirical performances of these estimators and tests are illustrated by Monte Carlo simulation for both non-contaminated and contaminated data.
doi_str_mv 10.1016/j.jmva.2010.07.010
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subjects Distribution theory
Estimating techniques
Exact sciences and technology
Location model
Location model Minimum divergence estimators Robust estimation Robust test Scale model
Mathematical functions
Mathematics
Minimum divergence estimators
Monte Carlo simulation
Multivariate analysis
Nonparametric inference
Parametric inference
Probability and statistics
Robust estimation
Robust test
Scale model
Sciences and techniques of general use
Statistics
Statistics Theory
Studies
title Dual divergence estimators and tests: Robustness results
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