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ASYMPTOTIC ANCILLARITY AND CONDITIONAL INFERENCE FOR STOCHASTIC-PROCESSES

SWEETING, TJ; (1992) ASYMPTOTIC ANCILLARITY AND CONDITIONAL INFERENCE FOR STOCHASTIC-PROCESSES. ANN STAT , 20 (1) 580 - 589.

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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.

Type:Article
Title:ASYMPTOTIC ANCILLARITY AND CONDITIONAL INFERENCE FOR STOCHASTIC-PROCESSES
Keywords:ASYMPTOTIC CONDITIONAL INFERENCE, ASYMPTOTIC ANCILLARITY, NONERGODIC MODELS, MAXIMUM LIKELIHOOD ESTIMATOR, SCORE STATISTIC
UCL classification:UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science

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