Empirical Likelihood and Quantile Methods for Time Series Efficiency, Robustness, Optimality, and Prediction /

• Main Authors: Liu, Yan (Author, http://id.loc.gov/vocabulary/relators/aut), Akashi, Fumiya (http://id.loc.gov/vocabulary/relators/aut), Taniguchi, Masanobu (http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Singapore : Springer Singapore : Imprint: Springer, 2018.
• Edition: 1st ed. 2018.
• Series: JSS Research Series in Statistics,
• Subjects: Statistics .; Statistical Theory and Methods.; Statistics for Business, Management, Economics, Finance, Insurance.; Statistics for Social Sciences, Humanities, Law.

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...

Table of Contents:

• 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.