Statistics Colloquium
January 25 (Fri) 11 a.m.
Alavi Commons Room, 6625 Everett Tower

Iterative Rank Estimation for Generalized Linear Models

Guy-Vanie Miakonkana
Department of Mathematics and Statistics
Auburn University

We consider the estimation of parameters of a generalized linear regression model. Our estimator is defined iteratively starting from an initial obtained by minimizing the Wilcoxon dispersion function for independent errors. We show that the iterative estimator converges to the rank version of the maximum quasi-likelihood estimator as the number of iterations increases. The consistency and the asymptotic normality of the rank version of the maximum quasi-likelihood estimator are given. As in the linear model, the procedure results in estimators that are robust in the response space. We present results of a simulation study as well as a real world data example to illustrate the robustness and efficiency of the estimator.

All statistics students are expected to attend.


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