Bootstrap selection of the smoothing parameter in nonparametric hazard bate estimation

• 成果类型: Article
• 署名作者: GonzalezManteiga, W; Cao, R; Marron, JS
• 署名单位: Universidade da Coruna; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2291732
• 发表日期: 1996
• 页码: 1130-1140
• 关键词: kernel density estimators; dependent censorship; Cross-validation; bandwidth; CHOICE; MODEL

摘要

An asymptotic representation of the mean weighted integrated squared error for the kernel-based estimator of the hazard rate in the presence of right-censored samples is obtained for different bootstrap resampling methods. As a consequence, a new bandwidth selector based on the bootstrap is introduced. Very satisfactory simulations results are obtained in comparison to the cross-validation selector for different models, using WARPed (i.e., binned) versions of the estimators.