Titlebook: Asymptotic Optimal Inference for Non-ergodic Models; Ishwar V. Basawa,David John Scott Book 1983 Springer-Verlag New York Inc. 1983 Branch

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Charles E. Burkhardt,Jacob J. Leventhalse models of non-ergodic type (see §2 for definitions), and results on efficiency of estimators and tests will be discussed using a unified approach. Our aim in this chapter is to present the main ideas and general asymptotic results in an informal manner. More detailed treatment of specific problems discussed here is given in subsequent chapters.

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https://doi.org/10.1007/978-1-4615-6205-4f a general non-ergodic model defined in terras of the non-local asymptotic behaviour of the log-likelihood ratio and discuss various applications. Also, extensions of Bahadur efficiency concepts to such models will be briefly indicated.

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Efficiency of Estimation,timators, there is an upper bound for the asymptotic concentration, such that the set of parameter values on which any particular estimator has higher concentration is of Lebesgue measure zero. The restriction placed on the class of competing estimators in order to assert the validity of the upper b

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