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Titlebook: Empirical Likelihood and Quantile Methods for Time Series; Efficiency, Robustne Yan Liu,Fumiya Akashi,Masanobu Taniguchi Book 2018 The Auth

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發(fā)表于 2025-3-21 16:21:43 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Empirical Likelihood and Quantile Methods for Time Series
副標(biāo)題Efficiency, Robustne
編輯Yan Liu,Fumiya Akashi,Masanobu Taniguchi
視頻videohttp://file.papertrans.cn/309/308865/308865.mp4
概述Deals with nonstandard settings such as infinite variance rather than weakly stationary time series.Demonstrates that methods for parameter estimation and hypotheses testing are essentially nonparamet
叢書(shū)名稱(chēng)SpringerBriefs in Statistics
圖書(shū)封面Titlebook: Empirical Likelihood and Quantile Methods for Time Series; Efficiency, Robustne Yan Liu,Fumiya Akashi,Masanobu Taniguchi Book 2018 The Auth
描述This book integrates the fundamentals of asymptotic theory of statistical inference for time series under nonstandard settings, e.g., infinite variance processes, not only from the point of view of efficiency but also from that of robustness and optimality by minimizing prediction error. This is the first book to consider the generalized empirical likelihood applied to time series models in frequency domain and also the estimation motivated by minimizing quantile prediction error without assumption of true model. It provides the reader with a new horizon for understanding the prediction problem that occurs in time series modeling and a contemporary approach of hypothesis testing by the generalized empirical likelihood method. Nonparametric aspects of the methods proposed in this book also satisfactorily address economic and financial problems without imposing redundantly strong restrictions on the model, which has been true until now. Dealing with infinite variance processes makesanalysis of economic and financial data more accurate under the existing results from the demonstrative research. The scope of applications, however, is expected to apply to much broader academic fields. T
出版日期Book 2018
關(guān)鍵詞Empirical Likelihood; Quantile Score; Heavy Tail; Efficiency; Robustness
版次1
doihttps://doi.org/10.1007/978-981-10-0152-9
isbn_softcover978-981-10-0151-2
isbn_ebook978-981-10-0152-9Series ISSN 2191-544X Series E-ISSN 2191-5458
issn_series 2191-544X
copyrightThe Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2018
The information of publication is updating

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Book 2018e processes, not only from the point of view of efficiency but also from that of robustness and optimality by minimizing prediction error. This is the first book to consider the generalized empirical likelihood applied to time series models in frequency domain and also the estimation motivated by mi
地板
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Professionalisierung in der Altenpflegefrequency domain and propose a test statistic associated with our quantile estimator, which asymptotically converges to the standard normal under the null hypothesis. The finite sample performance of the quantile estimator is shown in our numerical studies.
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發(fā)表于 2025-3-22 10:36:21 | 只看該作者
Pflegequalit?t und Qualit?tssicherungmpirical likelihood is asymptotically .-distributed. We discuss the application of the empirical likelihood method to symmetric .-stable linear processes. It is shown that the asymptotic distribution of our test statistic is quite different from the usual one. We illustrate the theoretical result with some numerical simulations.
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Introduction,proach to analyzing stationary time series. Prediction problems, including interpolation and extrapolation problems, are discussed for stationary time series. In particular, we clarify the construction of a robust linear interpolator and extrapolator.
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