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

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...

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Bibliographic Details
Main Authors: Liu, Yan, Akashi, Fumiya (Author), Taniguchi, Masanobu (Author)
Format: eBook
Published: Singapore Springer Nature Singapore 2018, 2018
Edition:1st ed. 2018
Series:JSS Research Series in Statistics
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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