Abstract
Lucien LeCam established the most important and sophisticated foundation of the general statistical asymptotic theory. He introduced the concept of local asymptotic normality (LAN) for the likelihood ratio of general statistical models. Once LAN is proved, the asymptotic optimality of estimators and tests is described in terms of the LAN property. In this chapter we review LeCam’s LAN theorem and show the LAN results for a wide class of vector linear processes, which are permitted to exhibit long-memory dependence.
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© 2000 Springer Science+Business Media New York
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Taniguchi, M., Kakizawa, Y. (2000). Local Asymptotic Normality for Stochastic Processes. In: Asymptotic Theory of Statistical Inference for Time Series. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1162-4_2
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DOI: https://doi.org/10.1007/978-1-4612-1162-4_2
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4612-7028-7
Online ISBN: 978-1-4612-1162-4
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