ASYMPTOTIC ANCILLARITY AND CONDITIONAL INFERENCE FOR STOCHASTIC-PROCESSES
成果类型：
Article
署名作者：
SWEETING, TJ
刊物名称：
ANNALS OF STATISTICS
ISSN/ISSBN：
0090-5364
DOI：
10.1214/aos/1176348542
发表日期：
1992
页码：
580-589
关键词：
摘要：
Simple conditions on the observed information ensure asymptotic normality of the conditional distributions of the randomly normed score statistic and maximum likelihood estimator given a suitable asymptotically ancillary statistic. In particular, asymptotic normality holds conditional on any asymptotically ancillary statistic asymptotically equivalent to observed information. The results apply to inference from a general stochastic process and are of particular relevance in the case of nonergodic models.