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.
Publié le : 1992-03-14
Classification:
Asymptotic conditional inference,
asymptotic ancillarity,
nonergodic models,
maximum likelihood estimator,
score statistic,
62F12,
62M99
@article{1176348542,
author = {Sweeting, Trevor J.},
title = {Asymptotic Ancillarity and Conditional Inference for Stochastic Processes},
journal = {Ann. Statist.},
volume = {20},
number = {1},
year = {1992},
pages = { 580-589},
language = {en},
url = {http://dml.mathdoc.fr/item/1176348542}
}
Sweeting, Trevor J. Asymptotic Ancillarity and Conditional Inference for Stochastic Processes. Ann. Statist., Tome 20 (1992) no. 1, pp. 580-589. http://gdmltest.u-ga.fr/item/1176348542/