Li-Hsien Sun, Xin-Wei Huang, Mohammed S. Alqawba

Copula-Based Markov Models for Time Series

Parametric Inference and Process Control

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• SpringerBriefs in Statistics
• Verlag: Springer / Springer Nature Singapore / Springer, Berlin
• Artikelnr. des Verlages: 978-981-15-4997-7
• 1st ed. 2020
• Seitenzahl: 148
• Erscheinungstermin: 2. Juli 2020
• Englisch
• Abmessung: 235mm x 155mm x 9mm
• Gewicht: 238g
• ISBN-13: 9789811549977
• ISBN-10: 9811549974
• Artikelnr.: 59046986

Autorenporträt

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

Inhaltsangabe

Chapter 1 Overview of the book with data examples. -Chapter 2 Copula and Markov models.- Chapter 3 Estimation, model diagnosis, and process control under the normal model.- Chapter 4 Estimation under the normal mixture model for financial time series data.- Chapter 5 Bayesian estimation under the t-distribution for financial time series data.- Chapter 6 Control charts of mean and variance using copula Markov SPC and conditional distribution by copula.- Chapter 7 Copula Markov models for count series with excess zeros.