Authors:
- Serves as introductory textbook on the analysis of time series data for students majoring in statistics and related fields
- Includes numerous real-world data examples as well as R codes for implementation
- Discusses times series data, from basic theories to real-world applications
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 (7 chapters)
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Front Matter
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Back Matter
About this book
This book provides statistical methodologies for time series data, focusing on copula-based Markov chain models for serially correlated time series. It also includes data examples from economics, engineering, finance, sport and other disciplines to illustrate the methods presented. An accessible textbook for students in the fields of economics, management, mathematics, statistics, and related fields wanting to gain insights into the statistical analysis of time series data using copulas, the book also features stand-alone chapters to appeal to researchers.
As the subtitle suggests, the book highlights parametric models based on normal distribution, t-distribution, normal mixture distribution, Poisson distribution, and others. Presenting likelihood-based methods as the main statistical tools for fitting the models, the book details the development of computing techniques to find the maximum likelihood estimator. It also addresses statistical process control, as well as Bayesian and regression methods. Lastly, to help readers analyze their data, it provides computer codes (R codes) for most of the statistical methods.
Authors and Affiliations
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Graduate Inst. of Statistics, National Central University, Taoyuan, Taiwan
Li-Hsien Sun
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National Chiao Tung University, Hsinchu, Taiwan
Xin-Wei Huang
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Qassim University, Unayzah, Saudi Arabia
Mohammed S. Alqawba
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Division of Science and Math, Uniersity of Minnesota at Morris, MORRIS, USA
Jong-Min Kim
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Chang Gung University, Taoyuan, Taiwan
Takeshi Emura
About the authors
Li-Hsien Sun, National Central University
Xin-Wei Huang, National Chiao Tung University
Mohammed S. Alqawba, Qassim University
Jong-Min Kim, University of Minnesota at Morris
Takeshi Emura, Chang Gung University
Bibliographic Information
Book Title: Copula-Based Markov Models for Time Series
Book Subtitle: Parametric Inference and Process Control
Authors: Li-Hsien Sun, Xin-Wei Huang, Mohammed S. Alqawba, Jong-Min Kim, Takeshi Emura
Series Title: SpringerBriefs in Statistics
DOI: https://doi.org/10.1007/978-981-15-4998-4
Publisher: Springer Singapore
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020
Softcover ISBN: 978-981-15-4997-7Published: 02 July 2020
eBook ISBN: 978-981-15-4998-4Published: 01 July 2020
Series ISSN: 2191-544X
Series E-ISSN: 2191-5458
Edition Number: 1
Number of Pages: XVI, 131
Number of Illustrations: 23 b/w illustrations, 11 illustrations in colour
Topics: Statistics for Business, Management, Economics, Finance, Insurance, Bioinformatics, Statistical Theory and Methods