MODIFIED POWER DIVERGENCE ESTIMATORS IN NORMAL MODELS - SIMULATION AND COMPARATIVE STUDY
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 phi-divergence is always equal to its upper bound, and the minimum phi-divergence estimates are trivial. Broniatowski and Vajda [3] 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 alpha epsilon 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 phi-divergence parameters.
minimum phi-divergence estimation robustness subdivergence superdivergence pc simulation relative efficiency