國立虎尾科技大學 |

Copula-Based Markov Models for Time Series = Parametric Inference and Process Control /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Copula-Based Markov Models for Time Series/ by Li-Hsien Sun, Xin-Wei Huang, Mohammed S. Alqawba, Jong-Min Kim, Takeshi Emura.
Reminder of title:	Parametric Inference and Process Control /
Author:	Sun, Li-Hsien.
other author:	Huang, Xin-Wei.
Description:	XVI, 131 p. 34 illus., 11 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Statistics . -
Online resource:	https://doi.org/10.1007/978-981-15-4998-4
ISBN:	9789811549984

Copula-Based Markov Models for Time Series = Parametric Inference and Process Control /
Sun, Li-Hsien.

Copula-Based Markov Models for Time SeriesParametric Inference and Process Control /[electronic resource] :by Li-Hsien Sun, Xin-Wei Huang, Mohammed S. Alqawba, Jong-Min Kim, Takeshi Emura. - 1st ed. 2020. - XVI, 131 p. 34 illus., 11 illus. in color.online resource. - JSS Research Series in Statistics,2364-0057. - JSS Research Series in Statistics,.

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.

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.

ISBN: 9789811549984

Standard No.: 10.1007/978-981-15-4998-4doiSubjects--Topical Terms:

1253516
Statistics .

LC Class. No.: QA276-280

Dewey Class. No.: 330.015195

Copula-Based Markov Models for Time Series = Parametric Inference and Process Control /
LDR:03152nam a22004095i 4500 001 1018392
003 DE-He213
005 20200703002658.0
007 cr nn 008mamaa
008 210318s2020 si | s |||| 0|eng d
020 $a 9789811549984 $9 978-981-15-4998-4
024 7 $a 10.1007/978-981-15-4998-4 $2 doi
035 $a 978-981-15-4998-4
050 4 $a QA276-280
072 7 $a PBT $2 bicssc
072 7 $a BUS061000 $2 bisacsh
072 7 $a PBT $2 thema
072 7 $a K $2 thema
082 0 4 $a 330.015195 $2 23
100 1 $a Sun, Li-Hsien. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1313374
245 1 0 $a Copula-Based Markov Models for Time Series $h [electronic resource] : $b Parametric Inference and Process Control / $c by Li-Hsien Sun, Xin-Wei Huang, Mohammed S. Alqawba, Jong-Min Kim, Takeshi Emura.
250 $a 1st ed. 2020.
264 1 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a XVI, 131 p. 34 illus., 11 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a JSS Research Series in Statistics, $x 2364-0057
505 0 $a 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.
520 $a 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.
650 0 $a Statistics . $3 1253516
650 0 $a Bioinformatics. $3 583857
650 1 4 $a Statistics for Business, Management, Economics, Finance, Insurance. $3 1211158
650 2 4 $a Statistical Theory and Methods. $3 671396
700 1 $a Huang, Xin-Wei. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1313375
700 1 $a Alqawba, Mohammed S. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1313376
700 1 $a Kim, Jong-Min. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1313377
700 1 $a Emura, Takeshi. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1202727
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811549977
776 0 8 $i Printed edition: $z 9789811549991
830 0 $a JSS Research Series in Statistics, $x 2364-0057 $3 1255493
856 4 0 $u https://doi.org/10.1007/978-981-15-4998-4
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)