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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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Spatial Median-Based Smoothed and Self-Weighted GEL Method for Vector Autoregressive Models,vior of the distributional tail of the innovation processes; hence, the results in this paper provide a feasible testing procedure for the hypothesis. Simulation experiments illustrate the finite sample performance of the proposed methods.
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,Generalized Linear Spectral Models for Locally Stationary Processes,gh information criteria. Change points are identified via a sequence of score tests. Consistency and asymptotic normality are proved for the parametric estimators considered in the paper, under weak assumptions on the time-varying parameters.
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wide variety of statistical models based on profound theory..This book compiles theoretical developments on statistical inference for time series and related models in honor of Masanobu Taniguchi‘s 70th birthday. It covers models such as long-range dependence models, nonlinear conditionally heterosc
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Rank Tests for Randomness Against Time-Varying MA Alternative, joint asymptotic normality of the proposed rank test statistics and log-likelihood ratio is established by making use of the local asymptotic normal property. Then, applying LeCam’s third lemma, the asymptotic normality of test statistic under the alternative is shown automatically.
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-Process Method for Change Point Problems in Time Series,roblem of testing for parameter changes in time series models based on this .-process method. As an example, we consider the parameter change problem in some linear time series models. Some possibilities for nonlinear models are also discussed.
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Spatial Median-Based Smoothed and Self-Weighted GEL Method for Vector Autoregressive Models,cesses. The self-weighted generalized empirical likelihood (GEL) estimator and test statistic for the hypotheses of the nonlinear restriction of the parameters are proposed. The limiting distributions of the GEL estimator and test statistic are derived under mild distributional conditions for the in
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Excess Mean of Power Estimator of Extreme Value Index, existing mean of order . (MOP) estimators over different thresholds. The asymptotic normalities of the MOP and EMP estimators for dependent observations are established under some mild conditions. We also develop consistent estimators for the asymptotic variances of the MOP and EMP estimators. Furt
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