國立虎尾科技大學 |

Empirical likelihood and quantile methods for time series = efficiency, robustness, optimality, and prediction /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Empirical likelihood and quantile methods for time series/ by Yan Liu, Fumiya Akashi, Masanobu Taniguchi.
Reminder of title:	efficiency, robustness, optimality, and prediction /
Author:	Liu, Yan.
other author:	Akashi, Fumiya.
Published:	Singapore :Springer Singapore : : 2018.,
Description:	x, 136 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Time-series analysis. -
Online resource:	https://doi.org/10.1007/978-981-10-0152-9
ISBN:	9789811001529

Empirical likelihood and quantile methods for time series = efficiency, robustness, optimality, and prediction /
Liu, Yan.

Empirical likelihood and quantile methods for time seriesefficiency, robustness, optimality, and prediction /[electronic resource] :by Yan Liu, Fumiya Akashi, Masanobu Taniguchi. - Singapore :Springer Singapore :2018. - x, 136 p. :ill., digital ;24 cm. - JSS research series in statistics,2364-0057. - JSS research series in statistics..

Chapter 1. Introduction to Nonstandard Analysis in Time Series Analysis -- Chapter 2. Parameter Estimation by Quantile Prediction Error -- Chapter 3. Hypotheses Testing by Generalized Empirical Likelihood for Stable Processes -- Chapter 4. Higher Order Efficiency of Generalized Empirical Likelihood for Dependent Data -- Chapter 5. Robust Aspects of Empirical Likelihood for Unified Prediction Error -- Chapter 6. Applications.

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.

ISBN: 9789811001529

Standard No.: 10.1007/978-981-10-0152-9doiSubjects--Topical Terms:

528412
Time-series analysis.

LC Class. No.: QA280 / .L589 2018

Dewey Class. No.: 519.55

Empirical likelihood and quantile methods for time series = efficiency, robustness, optimality, and prediction /
LDR:03137nam a2200337 a 4500 001 930574
003 DE-He213
005 20190513113541.0
006 m d
007 cr nn 008maaau
008 190627s2018 si s 0 eng d
020 $a 9789811001529 $q (electronic bk.)
020 $a 9789811001512 $q (paper)
024 7 $a 10.1007/978-981-10-0152-9 $2 doi
035 $a 978-981-10-0152-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA280 $b .L589 2018
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.55 $2 23
090 $a QA280 $b .L783 2018
100 1 $a Liu, Yan. $3 1060385
245 1 0 $a Empirical likelihood and quantile methods for time series $h [electronic resource] : $b efficiency, robustness, optimality, and prediction / $c by Yan Liu, Fumiya Akashi, Masanobu Taniguchi.
260 $a Singapore : $c 2018. $b Springer Singapore : $b Imprint: Springer,
300 $a x, 136 p. : $b ill., digital ; $c 24 cm.
490 1 $a JSS research series in statistics, $x 2364-0057
505 0 $a Chapter 1. Introduction to Nonstandard Analysis in Time Series Analysis -- Chapter 2. Parameter Estimation by Quantile Prediction Error -- Chapter 3. Hypotheses Testing by Generalized Empirical Likelihood for Stable Processes -- Chapter 4. Higher Order Efficiency of Generalized Empirical Likelihood for Dependent Data -- Chapter 5. Robust Aspects of Empirical Likelihood for Unified Prediction Error -- Chapter 6. Applications.
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. $3 528412
650 0 $a Finance $x Econometric models. $3 557154
650 1 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Statistics for Business, Management, Economics, Finance, Insurance. $3 1211158
650 2 4 $a Statistics for Social Sciences, Humanities, Law. $3 1211304
700 1 $a Akashi, Fumiya. $3 1211827
700 1 $a Taniguchi, Masanobu. $3 1025171
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a JSS research series in statistics. $3 1211828
856 4 0 $u https://doi.org/10.1007/978-981-10-0152-9
950 $a Mathematics and Statistics (Springer-11649)