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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