Abstract
In this chapter we discuss optimal asymptotic tests for simple and composite hypotheses involving a scalar or vector parameter. The basic model is as given in §2 of Chapter 1 and we assume the LAMN condition is satisfied. This general model is used in §§3 and 4. In later sections more restrictive conditions are required. It turns out that the usual statistics such as the Rao’s score statistic, the Neyman statistic, and the likelihood-ratio (LR) statistic exhibit non-standard asymptotic behaviour in the non-ergodic case, as regards efficiency and limit distributions.
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© 1983 Springer-Verlag New York Inc.
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Basawa, I.V., Scott, D.J. (1983). Optimal Asymptotic Tests. In: Asymptotic Optimal Inference for Non-ergodic Models. Lecture Notes in Statistics, vol 17. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-5505-5_4
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DOI: https://doi.org/10.1007/978-1-4612-5505-5_4
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-90810-6
Online ISBN: 978-1-4612-5505-5
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