Optimal bandwidth selection for multivariate kernel deconvolution density estimation

被引：0
作者：
Élie Youndjé
Martin T. Wells
机构：
[1] Université de Rouen,Laboratoire de Mathématiques Raphaël Salem
[2] Cornell University,Department of Social Statistics
来源：
TEST | 2008年 / 17卷
关键词：
Density estimation; Deconvolution; Cross-validation; Asymptotic optimality; 62F03; 62E17; 62P25;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Assume we have i.i.d. replications from the mismeasured random vector Y=X+ε, where X and ε are mutually independent. We consider a data-driven bandwidth, based on a cross-validation ideas, for multivariate kernel deconvolution estimator of the density of X. The proposed data-driven bandwidth selection method is shown to be asymptotically optimal. As a by-product of the proof of this result, we show that the average squared error, the integrated squared error, and the mean integrated squared error are asymptotically equivalent error measures.
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页码：138 / 162
页数：24
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