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Asymptotic Optimal Inference for Non-Ergodic Models

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Asymptotic Optimal Inference for Non-Ergodic Models - Basawa, I V, and Scott, D J
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This monograph contains a comprehensive account of the recent work of the authors and other workers on large sample optimal inference for non-ergodic models. The non-ergodic family of models can be viewed as an extension of the usual Fisher-Rao model for asymptotics, referred to here as an ergodic family. The main feature of a non-ergodic model is that the sample Fisher information, appropriately normed, converges to a non-degenerate random variable rather than to a constant. Mixture experiments, growth models such as birth ...

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Asymptotic Optimal Inference for Non-Ergodic Models 1983, Springer, New York, NY

ISBN-13: 9780387908106

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