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

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Pediatric Traumatic Head Injuries constructing an estimator is to replace an unknown spectral density by the periodogram based on the data. The functional of interest is, however, not always linear with respect to the spectral density. For the nonlinear case replacing the unknown spectral density by the periodogram causes asymptoti

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Foundations of Sport-Related Brain Injuriesn 7.1 we give a basic formulation of discriminant analysis. We begin with the standard methods from classical multivariate analysis and then introduce the frequency domain approach in time series analysis (Section 7.2). Even if the Gaussianity of the process is not assumed, we can construct the Gaus

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Higher Order Asymptotic Theory for Stochastic Processes,We have already seen that various typical estimators and tests have the same limiting behavior. Thus it is required to illuminate their distinction. In this chapter, using higher order approximations (Edgeworth expansions) of the distribution of estimators and tests we discuss their higher order asymptotic optimality.

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https://doi.org/10.1007/978-94-011-3814-7for 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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Local Asymptotic Normality for Stochastic Processes,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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