Robust Inference Using the Exponential-Polynomial Divergence - Singh, Pushpinder; Mandal, Abhijit; Basu, Ayanendranath (Journal of statistical theory and practice, 2021)
Density-based minimum divergence procedures represent popular techniques in parametric statistical inference. They combine strong robustness properties with high (sometimes full) asymptotic efficiency. Among density-based minimum distance procedures, the methods based on the Brègman divergence have the attractive property that the empirical formulation of the divergence does not require the use of any nonparametric smoothing technique such as kernel density estimation. The methods based on the density power divergence (DPD) represent the current standard in this area of research. In this paper, we will present a more generalized divergence which subsumes the DPD as a special case, and produces several new options providing better compromises between robustness and efficiency.