Detroit, MI 48202
Speaker: Abhijit Mandal, Wayne State University
Title: Robust Tests Using the Density Power Divergence
In any parametric inference problem, 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.
A part of this talk will be based on the following papers:
1. A. Basu, A. Mandal, N. Martin and L. Pardo (2013). Testing Statistical Hypotheses based on the Density Power Divergence. Annals of the Institute of Statistical Mathematics, Vol. 65, 319-348.
2. A. Ghosh, A. Mandal, N. Martin and L. Pardo (2016). Influence Analysis of Robust Wald-type Tests. Journal of Multivariate Analysis, Vol. 147, 102-126.