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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
Stable URL: http://www.jstor.org/stable/2242180
Page Count: 10
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Asymptotic Ancillarity and Conditional Inference for Stochastic Processes
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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.

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