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Asymptotic Ancillarity and Conditional Inference for Stochastic Processes
Trevor J. Sweeting
The Annals of Statistics
Vol. 20, No. 1 (Mar., 1992), pp. 580-589
Published by: Institute of Mathematical Statistics
Stable URL: http://www.jstor.org/stable/2242180
Page Count: 10
You can always find the topics here!Topics: Inference, Statism, Statistics, Statistical theories, Probabilities, Local maximum, Stochastic processes, Maximum likelihood estimators, Statistical models, Density
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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.
The Annals of Statistics © 1992 Institute of Mathematical Statistics