Activity Number:
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339
- Model-Fitting, Likelihood-Based Inference, and Their Applications
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Type:
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Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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Abstract #324287
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Title:
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Robust Tests Using a Divergence Measure
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Author(s):
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Abhijit Mandal* and Ayanendranath Basu and Leandro Pardo and Nirian Martin
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Companies:
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Wayne State University and Indian Statistical Institute and Complutense University of Madrid and Complutense University of Madrid
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Keywords:
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Robust inference ;
Divergence measure ;
Testing of hypothesis
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Abstract:
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In any parametric inference the robustness of the procedure is a real concern. A procedure which retains a high degree of efficiency under the model and simultaneously provides stable inference under data contamination is preferable in any practical situation over another procedure which achieves its efficiency at the cost of robustness or vice versa. The density power divergence family of Basu et al. (Biometrika, 1998) provides a flexible class of divergences where the adjustment between efficiency and robustness is controlled by a single parameter $\beta$. In this talk, I will consider general tests of parametric hypotheses based on the density power divergence. The asymptotic null distribution and the robustness properties of the test statistic will be explored. The performance of the test will be explored through simulations and real data analysis.
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Authors who are presenting talks have a * after their name.