Empirical likelihood and quantile methods for time series : efficiency, robustness, optimality, and prediction /
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| Author / Creator: | Liu, Yan, author. |
|---|---|
| Imprint: | Singapore : Springer, [2018] ©2018 |
| Description: | 1 online resource |
| Language: | English |
| Series: | Springer briefs in statistics. JSS research series in statistics SpringerBriefs in statistics. JSS research series in statistics. |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/11761239 |
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| 100 | 1 | |a Liu, Yan, |e author. | |
| 245 | 1 | 0 | |a Empirical likelihood and quantile methods for time series : |b efficiency, robustness, optimality, and prediction / |c Yan Liu, Fumiya Akashi, Masanobu Taniguchi. |
| 264 | 1 | |a Singapore : |b Springer, |c [2018] | |
| 264 | 4 | |c ©2018 | |
| 300 | |a 1 online resource | ||
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| 490 | 1 | |a Springer briefs in statistics. JSS research series in statistics | |
| 504 | |a Includes bibliographical references and index. | ||
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| 505 | 0 | |a Intro; Preface; Contents; 1 Introduction; 1.1 Stationary Time Series; 1.2 Prediction Problem; 1.3 Interpolation and Extrapolation Problem; 1.4 Robust Interpolation and Extrapolation; 2 Parameter Estimation Based on Prediction; 2.1 Introduction; 2.1.1 Location Disparity; 2.1.2 Scale Disparity; 2.1.3 A New Disparity Based on Prediction; 2.2 Fundamentals of the New Disparity; 2.3 Parameter Estimation Based on Disparity; 2.3.1 Finite Variance Innovations Case; 2.3.2 Infinite Variance Innovations Case; 2.4 Efficiency and Robustness; 2.4.1 Robustness Against the Fourth-Order Cumulant | |
| 505 | 8 | |a 5 Self-weighted GEL Methods for Infinite Variance Processes5.1 Introduction to Self-weighted Least Absolute Deviations Approach; 5.2 Self-weighted GEL Statistics; 5.3 Application to the Change Point Test; 5.4 Numerical Studies; 5.5 Auxiliary Results; Bibliography; ; Index | |
| 520 | |a 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 makes analysis 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. The methods are also sufficiently flexible in that they represent an advanced and unified development of prediction form including multiple-point extrapolation, interpolation, and other incomplete past forecastings. Consequently, they lead readers to a good combination of efficient and robust estimate and test, and discriminate pivotal quantities contained in realistic time series models. | ||
| 650 | 0 | |a Time-series analysis. |0 http://id.loc.gov/authorities/subjects/sh85135430 | |
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| 700 | 1 | |a Akashi, Fumiya, |e author. | |
| 700 | 1 | |a Taniguchi, Masanobu, |e author. | |
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| 880 | 8 | |6 505-00/(S |a 2.4.2 Asymptotic Efficiency2.4.3 Robustness Against Randomly Missing Observations; 2.5 Numerical Studies; 2.5.1 Relative Efficiency; 2.5.2 Robustness of the Disparity; 3 Quantile Method for Time Series; 3.1 Introduction; 3.2 Preliminaries; 3.3 Quantile Estimation; 3.4 Sinusoid Model; 3.5 Quantile Test; 3.6 Numerical Studies; 3.6.1 Finite Sample Performance; 3.6.2 Numerical Results for Estimation; 4 Empirical Likelihood Method for Time Series; 4.1 Introduction; 4.2 Empirical Likelihood in the Frequency Domain; 4.3 Empirical Likelihood for Symmetric α-stable Processes; 4.4 Numerical Studies | |
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