Copula-Based Markov Models for Time Series: Parametric Inference and Process Control (SpringerBriefs in Statistics) - Softcover

Sun, Li-Hsien; Huang, Xin-Wei; Alqawba, Mohammed S.; Kim, Jong-Min; Emura, Takeshi

Softcover

ISBN 10: 9811549974 ISBN 13: 9789811549977

Publisher: Springer, 2020

Synopsis

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.

About this title

About the Author

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

Edition details

• Publisher: Springer
• Publication date: 2020
• Language: English
• ISBN 10: 9811549974
• ISBN 13: 9789811549977
• Binding: Paperback
• Edition number: 1
• Number of pages: 147