论文标题
使用指数性化度差异的鲁棒推理
Robust Inference Using the Exponential-Polynomial Divergence
论文作者
论文摘要
基于密度的最小差异程序代表参数统计推断中的流行技术。它们将强大的鲁棒性特性与较高（有时是完整）的渐近效率相结合。在基于密度的最小距离程序中，基于布雷格曼 - 差异的方法具有有吸引力的特性，即发散的经验表述不需要使用任何非参数平滑技术，例如内核密度估计。基于密度功率差异（DPD）的方法代表了这一研究领域的当前标准。在本文中，我们将提出更广泛的差异，将DPD作为一种特殊情况，并产生多种新的选择，从而在鲁棒性和效率之间提供更好的妥协。
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 Bregman-divergence have the attractive property that the empirical formulation of the divergence does not require the use of any non-parametric 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 that subsumes the DPD as a special case and produces several new options providing better compromises between robustness and efficiency.
