A Class of Smooth Estimators for Discrete Distributions
提出一类用于离散分布的光滑权重函数估计量，在温和正则条件下具有强相合性和渐近正态性，并通过大样本理论和小样本模拟表明其均方误差通常显著小于最大似然估计。
This paper presents a class of smooth weight function estimators for discrete distributions. Any estimator in the class depends on choosing a parameterized set of weights. The resulting estimators are strongly consistent and asymptotically normal under mild regularity conditions. A general procedure for choosing the weight function smoothing parameter is given along with specific solutions in some cases. Mean squared error comparisons with the maximum likelihood estimator based on large-sample theory and small-sample simulations are obtained. Typically, the weight function estimates yield significantly smaller mean squared error in these comparisons.