Copula-Based Markov Models for Time Series: Parametric Inference and Process Control
Language: English
Published by Springer Singapore, 2020
- Softcover
- New
Seller: moluna, Greven, Germanymoluna
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Condition: New
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Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 .
Seller Inventory # 362613500
- Title
- Copula-Based Markov Models for Time Series: Parametric Inference and Process Control
- Author
- Li-Hsien Sun|Xin-Wei Huang|Mohammed S. Alqawba|Jong-Min Kim|Takeshi Emura
- Publisher
- Springer Singapore
- Publication year
- 2020
- Condition
- New
- Binding
- Kartoniert / Broschiert
- Language
- English
- ISBN 10
- 9811549974
- ISBN 13
- 9789811549977
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.
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