MAXIMUM SMOOTHED LIKELIHOOD ESTIMATORS FOR THE INTERVAL CENSORING MODEL

• 成果类型: Article
• 署名作者: Groeneboom, Piet
• 署名单位: Delft University of Technology
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/14-AOS1256
• 发表日期: 2014
• 页码: 2092-2137
• 关键词: asymptotically optimal estimation; functionals

摘要

We study the maximum smoothed likelihood estimator (MSLE) for interval censoring, case 2, in the so-called separated case. Characterizations in terms of convex duality conditions are given and strong consistency is proved. Moreover, we show that, under smoothness conditions on the underlying distributions and using the usual bandwidth choice in density estimation, the local convergence rate is n(-2/5) and the limit distribution is normal, in contrast with the rate n(-1/3) of the ordinary maximum likelihood estimator.