On locally adaptive density estimation
成果类型：
Article
署名作者：
Sain, SR; Scott, DW
署名单位：
Rice University
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.2307/2291578
发表日期：
1996
页码：
1525-1534
关键词：
摘要：
Theoretical and practical aspects of the sample-point adaptive positive kernel density estimator are examined. A closed-form expression for the mean integrated squared error is obtained through the device of preprocessing the data by binning. With this expression, the exact behavior of the optimally adaptive smoothing parameter function is studied for the first time. The approach differs from most earlier techniques in that bias of the adaptive estimator remains O(h(2)) and is not ''improved'' to the rate O(h(4)). A practical algorithm is constructed using a modification of least squares cross-validation. Simulated and real examples are presented, including comparisons with a fixed bandwidth estimator and a fully automatic version of Abramson's adaptive estimator. The results are very promising.