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Titlebook: Diagnostic Methods in Time Series; Fumiya Akashi,Masanobu Taniguchi,Tomoyuki Amano Book 2021 The Author(s), under exclusive license to Spr

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書目名稱Diagnostic Methods in Time Series
編輯Fumiya Akashi,Masanobu Taniguchi,Tomoyuki Amano
視頻videohttp://file.papertrans.cn/271/270691/270691.mp4
概述Covers a broad range of techniques for model diagnostics of time series models under general settings.Provides robust testing procedures including variable selection and causality without any moment c
叢書名稱SpringerBriefs in Statistics
圖書封面Titlebook: Diagnostic Methods in Time Series;  Fumiya Akashi,Masanobu Taniguchi,Tomoyuki Amano Book 2021 The Author(s), under exclusive license to Spr
描述This book contains new aspects of model diagnostics in time series analysis, including variable selection problems and higher-order asymptotics of tests. This is the first book to cover systematic approaches and widely applicable results for nonstandard models including infinite variance processes. The book begins by introducing a unified view of a portmanteau-type test based on a likelihood ratio test, useful to test general parametric hypotheses inherent in statistical models. The conditions for the limit distribution of portmanteau-type tests to be asymptotically pivotal are given under general settings, and very clear implications for the relationships between the parameter of interest and the nuisance parameter are elucidated in terms of Fisher-information matrices. A robust testing procedure against heavy-tailed time series models is also constructed in the context of variable selection problems. The setting is very reasonable in the context of financial data analysis and econometrics, and the result is applicable to causality tests of heavy-tailed time series models. In the last two sections, Bartlett-type adjustments for a class of test statistics are discussed when the par
出版日期Book 2021
關(guān)鍵詞Time Series Analysis; Infinite Variance Process; Variable Selection; Test of Causality; Empirical Likeli
版次1
doihttps://doi.org/10.1007/978-981-16-2264-9
isbn_softcover978-981-16-2263-2
isbn_ebook978-981-16-2264-9Series ISSN 2191-544X Series E-ISSN 2191-5458
issn_series 2191-544X
copyrightThe Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
The information of publication is updating

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https://doi.org/10.1007/978-1-4419-7085-5ic, called the self-weighted GEL statistic in the time domain. The limiting distribution of the self-weighted GEL statistic?is shown to be the usual chi-squared one regardless of whether the model has finite variance or not.
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Robust Causality Test of Infinite Variance Processes,ic, called the self-weighted GEL statistic in the time domain. The limiting distribution of the self-weighted GEL statistic?is shown to be the usual chi-squared one regardless of whether the model has finite variance or not.
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https://doi.org/10.1007/978-981-16-2264-9Time Series Analysis; Infinite Variance Process; Variable Selection; Test of Causality; Empirical Likeli
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978-981-16-2263-2The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
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