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March, 1992 Asymptotic Ancillarity and Conditional Inference for Stochastic Processes
Trevor J. Sweeting
Ann. Statist. 20(1): 580-589 (March, 1992). DOI: 10.1214/aos/1176348542


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.


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Trevor J. Sweeting. "Asymptotic Ancillarity and Conditional Inference for Stochastic Processes." Ann. Statist. 20 (1) 580 - 589, March, 1992.


Published: March, 1992
First available in Project Euclid: 12 April 2007

zbMATH: 0757.62013
MathSciNet: MR1150364
Digital Object Identifier: 10.1214/aos/1176348542

Primary: 62F12
Secondary: 62M99

Keywords: asymptotic ancillarity , Asymptotic conditional inference , maximum likelihood estimator , nonergodic models , score statistic

Rights: Copyright © 1992 Institute of Mathematical Statistics

Vol.20 • No. 1 • March, 1992
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