LEADER 04205nam 22005535 450 001 9910303454503321 005 20250408070937.0 010 $a981-10-0152-9 024 7 $a10.1007/978-981-10-0152-9 035 $a(CKB)4100000007204775 035 $a(MiAaPQ)EBC5611148 035 $a(DE-He213)978-981-10-0152-9 035 $a(PPN)232961573 035 $a(EXLCZ)994100000007204775 100 $a20181205d2018 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEmpirical Likelihood and Quantile Methods for Time Series $eEfficiency, Robustness, Optimality, and Prediction /$fby Yan Liu, Fumiya Akashi, Masanobu Taniguchi 205 $a1st ed. 2018. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2018. 215 $a1 online resource (144 pages) 225 1 $aJSS Research Series in Statistics,$x2364-0065 311 08$a981-10-0151-0 327 $aChapter 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. 330 $aThis book integrates the fundamentals of asymptotic theory of statistical inference for time series under nonstandard settings, e.g., infinite variance processes, not only from the point of view of efficiency but also from that of robustness and optimality by minimizing prediction error. This is the first book to consider the generalized empirical likelihood applied to time series models in frequency domain and also the estimation motivated by minimizing quantile prediction error without assumption of true model. It provides the reader with a new horizon for understanding the prediction problem that occurs in time series modeling and a contemporary approach of hypothesis testing by the generalized empirical likelihood method. Nonparametric aspects of the methods proposed in this book also satisfactorily address economic and financial problems without imposing redundantly strong restrictions on the model, which has been true until now. Dealing with infinite variance processes makesanalysis of economic and financial data more accurate under the existing results from the demonstrative research. The scope of applications, however, is expected to apply to much broader academic fields. The methods are also sufficiently flexible in that they represent an advanced and unified development of prediction form including multiple-point extrapolation, interpolation, and other incomplete past forecastings. Consequently, they lead readers to a good combination of efficient and robust estimate and test, and discriminate pivotal quantities contained in realistic time series models. 410 0$aJSS Research Series in Statistics,$x2364-0065 606 $aStatistics 606 $aStatistics 606 $aSocial sciences$xStatistical methods 606 $aStatistical Theory and Methods 606 $aStatistics in Business, Management, Economics, Finance, Insurance 606 $aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy 615 0$aStatistics. 615 0$aStatistics. 615 0$aSocial sciences$xStatistical methods. 615 14$aStatistical Theory and Methods. 615 24$aStatistics in Business, Management, Economics, Finance, Insurance. 615 24$aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. 676 $a519.55 700 $aLiu$b Yan$4aut$4http://id.loc.gov/vocabulary/relators/aut$0652380 702 $aAkashi$b Fumiya$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aTaniguchi$b Masanobu$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910303454503321 996 $aEmpirical Likelihood and Quantile Methods for Time Series$92238285 997 $aUNINA