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Titlebook: Research Papers in Statistical Inference for Time Series and Related Models; Essays in Honor of M Yan Liu,Junichi Hirukawa,Yoshihide Kakiza

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-Process Method for Change Point Problems in Time Series,blems not only for ergodic models but also for some non-ergodic models where the Fisher information matrix is random. In this paper, we consider the problem of testing for parameter changes in time series models based on this .-process method. As an example, we consider the parameter change problem
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,Copula Bounds for Circular Data,m variables. In two dimensions, the Fréchet–Hoeffding upper (lower) bound indicates the perfect positive (negative) dependence between two random variables. However, for circular random variables, the usual concept of dependency may not be accepted because of their periodicity. In this paper, we red
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,Orthogonal Impulse Response Analysis in Presence of Time-Varying Covariance,or vector is not constant in time. The usual approach for taking such covariance behavior into account consists in applying the standard tools to sub-periods of the whole sample. We underline that such a practice may lead to severe upward bias. We propose a new approach intended to give what we argu
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A Simple Isotropic Correlation Family in , with Long-Range Dependence and Flexible Smoothness,ts a new family of isotropic correlation functions whose members display long-range dependence and can also model different degrees of smoothness. This family is compared to a sub-family of the Matérn family commonly used in geostatistics, and two other recently proposed families of covariance functions with long-range dependence are discussed.
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