Double one-sided cross-validation of local linear hazards

• 成果类型: Article
• 署名作者: Luz Gamiz, Maria; Mammen, Enno; Martinez Miranda, Maria Dolores; Nielsen, Jens Perch
• 署名单位: University of Granada; Ruprecht Karls University Heidelberg; HSE University (National Research University Higher School of Economics); City St Georges, University of London
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12133
• 发表日期: 2016
• 页码: 755-779
• 关键词: kernel density-estimation; smoothing parameter; selection; INFORMATION; lifetables

摘要

The paper brings together the theory and practice of local linear kernel hazard estimation. Bandwidth selection is fully analysed, including double one-sided cross-validation that is shown to have good practical and theoretical properties. Insight is provided into the choice of the weighting function in the local linear minimization and it is pointed out that classical weighting sometimes lacks stability. A new semiparametric hazard estimator transforming the survival data before smoothing is introduced and shown to have good practical properties.