Overview
- Deals with nonstandard settings such as infinite variance rather than weakly stationary time series
- Demonstrates that methods for parameter estimation and hypotheses testing are essentially nonparametric so that they are appropriate for economics and finance
- Explains that the methods are advanced and unified developments of multiple-point extrapolation and interpolation in frequency domain
Part of the book series: SpringerBriefs in Statistics (BRIEFSSTATIST)
Part of the book sub series: JSS Research Series in Statistics (JSSRES)
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Table of contents (5 chapters)
Keywords
About this book
Reviews
Authors and Affiliations
About the authors
Fumiya Akashi, Dr., Waseda University, f.akashi@kurenai.waseda.jp, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
Masanobu Taniguchi, Professor, Research Importance Position, Research Institute for Science & Engineering, Waseda University, taniguchi@waseda.jp, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
Bibliographic Information
Book Title: Empirical Likelihood and Quantile Methods for Time Series
Book Subtitle: Efficiency, Robustness, Optimality, and Prediction
Authors: Yan Liu, Fumiya Akashi, Masanobu Taniguchi
Series Title: SpringerBriefs in Statistics
DOI: https://doi.org/10.1007/978-981-10-0152-9
Publisher: Springer Singapore
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2018
Softcover ISBN: 978-981-10-0151-2Published: 17 December 2018
eBook ISBN: 978-981-10-0152-9Published: 05 December 2018
Series ISSN: 2191-544X
Series E-ISSN: 2191-5458
Edition Number: 1
Number of Pages: X, 136
Number of Illustrations: 1 b/w illustrations, 9 illustrations in colour
Topics: Statistical Theory and Methods, Statistics for Business, Management, Economics, Finance, Insurance, Statistics for Social Sciences, Humanities, Law