Titlebook: Asymptotic Theory of Statistical Inference for Time Series; Masanobu Taniguchi,Yoshihide Kakizawa Book 2000 Springer Science+Business Medi

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Lecture Notes in Computer Sciencestochastic processes will be reviewed. Because the statistical analysis for stochastic processes largely relies on the asymptotic theory, we also explain some useful limit theorems and central limit theorems. We have placed some fundamental results of mathematics, probability, and statistics in the Appendix.

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Elements of Stochastic Processes,stochastic processes will be reviewed. Because the statistical analysis for stochastic processes largely relies on the asymptotic theory, we also explain some useful limit theorems and central limit theorems. We have placed some fundamental results of mathematics, probability, and statistics in the Appendix.

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0172-7397 tion principle, and saddlepoint approximation. Because it is d­ ifficult to use the exact distribution theory, the discussion is based on the asymptotic theory.978-1-4612-7028-7978-1-4612-1162-4Series ISSN 0172-7397 Series E-ISSN 2197-568X

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Asymptotic Theory of Estimation and Testing for Stochastic Processes,odels and the asymptotic estimation theory based on the conditional least squares estimator and maximum likelihood estimator (MLE). We address the problem of statistical model selection in general fashion. Also the asymptotic theory for nonergodic models is mentioned. Recently much attention has bee

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Asymptotic Theory for Long-Memory Processes,1968, 1969a, b)) claimed that Hurst’s findings could be modeled by them. Since then, a lot of probabilistic and statistical methods have been brought in long-memory processes (see Beran (1994a) and Robinson (1994a)). Interestingly, the illuminated results are often different from those for ordinary

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