Local Likelihood Based on Kernel Censoring
借鉴删失数据似然的思想，提出一种局部似然函数，对样本空间中靠近感兴趣区域的观测赋予更高权重，用于评估参数模型的局部偏离和半参数密度估计。
SUMMARY By drawing an analogy with likelihood for censored data, a local likelihood function is proposed which gives more weight to observations near a region of interest in the sample space. Resulting methods can be used for assessing local departures from a parametric model, and for semiparametric density estimation. Some theory, and three examples, is given.