The Annals of Statistics

Asymptotic Ancillarity and Conditional Inference for Stochastic Processes

Trevor J. Sweeting

Full-text: Open access

Abstract

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.

Article information

Source
Ann. Statist., Volume 20, Number 1 (1992), 580-589.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176348542

Digital Object Identifier
doi:10.1214/aos/1176348542

Mathematical Reviews number (MathSciNet)
MR1150364

Zentralblatt MATH identifier
0757.62013

JSTOR
links.jstor.org

Subjects
Primary: 62F12: Asymptotic properties of estimators
Secondary: 62M99: None of the above, but in this section

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

Citation

Sweeting, Trevor J. Asymptotic Ancillarity and Conditional Inference for Stochastic Processes. Ann. Statist. 20 (1992), no. 1, 580--589. doi:10.1214/aos/1176348542. https://projecteuclid.org/euclid.aos/1176348542


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