國立高雄大學圖資館 |

Language: English

Back

Monte-Carlo simulation-based statist...

Chen, Ding-Geng ((Din))

Monte-Carlo simulation-based statistical modeling

Record Type:	Electronic resources : Monograph/item
Title/Author:	Monte-Carlo simulation-based statistical modelingedited by Ding-Geng (Din) Chen, John Dean Chen.
other author:	Chen, Ding-Geng
Published:	Singapore :Springer Singapore :2017.
Description:	xx, 430 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Monte Carlo method.
Online resource:	http://dx.doi.org/10.1007/978-981-10-3307-0
ISBN:	9789811033070$q(electronic bk.)

Monte-Carlo simulation-based statistical modeling
Monte-Carlo simulation-based statistical modeling[electronic resource] /edited by Ding-Geng (Din) Chen, John Dean Chen. - Singapore :Springer Singapore :2017. - xx, 430 p. :ill., digital ;24 cm. - ICSA book series in statistics,2199-0980. - ICSA book series in statistics..

Part 1: Monte-Carlo Techniques -- 1. Overview of Monte-Carlo Techniques -- 2. On Improving the Efficiency of the Monte-Carlo Methods Using Ranked Simulated Approach -- 3. Joint generation of Different Types of Data with Specified Marginal and Association Structures for Simulation Purposes -- 4. Quantifying the Uncertainty in Optimal Experimental Schemes via Monte-Carlo Simulations -- 5. Normal and Non-normal Data Simulations for the Evaluation of Two-sample Location Tests -- 6. Understanding dichotomization from Monte-Carlo Simulations -- Part 2: Monte-Carlo Methods in Missing Data -- 7. Hybrid Monte-Carlo in Multiple Missing Data Imputations with Application to a Bone Fracture Data -- 8. Methods for Handling Incomplete Longitudinal Data due to Missing at Random Dropout -- 9. Applications of Simulation for Missing Data Issues in Longitudinal Clinical Trials -- 10. Application of Markov Chain Monte Carlo Multiple Imputation Method to Deal with Missing Data From the Mechanism of MNAR in Sensitivity Analysis for a Longitudinal Clinical Trial -- 11. Fully Bayesian Methods for Missing Data under Ignitability Assumption -- Part 3: Monte-Carlo in Statistical Modellings -- 12. Markov-Chain Monte-Carlo Methods in Statistical modelling -- 13. Monte-Carlo Simulation in Modeling for Hierarchical Linear Mixed Models -- 14. Monte-Carlo Simulation of Correlated Binary Responses -- 15. Monte Carlo Methods in Financial Modeling -- 16. Bayesian Intensive Computations in Elliptical Models.

This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications. It is divided into three parts, with the first providing an overview of Monte-Carlo techniques, the second focusing on missing data Monte-Carlo methods, and the third addressing Bayesian and general statistical modeling using Monte-Carlo simulations. The data and computer programs used here will also be made publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, and to readily apply them in their own research. Featuring highly topical content, the book has the potential to impact model development and data analyses across a wide spectrum of fields, and to spark further research in this direction.

ISBN: 9789811033070$q(electronic bk.)

Standard No.: 10.1007/978-981-10-3307-0doiSubjects--Topical Terms:

186286
Monte Carlo method.

LC Class. No.: QA298

Dewey Class. No.: 518.282

Monte-Carlo simulation-based statistical modeling
LDR:03443nmm a2200337 a 4500 001 507553
003 DE-He213
005 20170829095802.0
006 m d
007 cr nn 008maaau
008 171030s2017 si s 0 eng d
020 $a 9789811033070$q(electronic bk.)
020 $a 9789811033063$q(paper)
024 7 $a 10.1007/978-981-10-3307-0 $2 doi
035 $a 978-981-10-3307-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QA298
072 7 $a PBT $2 bicssc
072 7 $a MBNS $2 bicssc
072 7 $a MED090000 $2 bisacsh
082 0 4 $a 518.282 $2 23
090 $a QA298 $b .M772 2017
245 0 0 $a Monte-Carlo simulation-based statistical modeling $h [electronic resource] / $c edited by Ding-Geng (Din) Chen, John Dean Chen.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2017.
300 $a xx, 430 p. : $b ill., digital ; $c 24 cm.
490 1 $a ICSA book series in statistics, $x 2199-0980
505 0 $a Part 1: Monte-Carlo Techniques -- 1. Overview of Monte-Carlo Techniques -- 2. On Improving the Efficiency of the Monte-Carlo Methods Using Ranked Simulated Approach -- 3. Joint generation of Different Types of Data with Specified Marginal and Association Structures for Simulation Purposes -- 4. Quantifying the Uncertainty in Optimal Experimental Schemes via Monte-Carlo Simulations -- 5. Normal and Non-normal Data Simulations for the Evaluation of Two-sample Location Tests -- 6. Understanding dichotomization from Monte-Carlo Simulations -- Part 2: Monte-Carlo Methods in Missing Data -- 7. Hybrid Monte-Carlo in Multiple Missing Data Imputations with Application to a Bone Fracture Data -- 8. Methods for Handling Incomplete Longitudinal Data due to Missing at Random Dropout -- 9. Applications of Simulation for Missing Data Issues in Longitudinal Clinical Trials -- 10. Application of Markov Chain Monte Carlo Multiple Imputation Method to Deal with Missing Data From the Mechanism of MNAR in Sensitivity Analysis for a Longitudinal Clinical Trial -- 11. Fully Bayesian Methods for Missing Data under Ignitability Assumption -- Part 3: Monte-Carlo in Statistical Modellings -- 12. Markov-Chain Monte-Carlo Methods in Statistical modelling -- 13. Monte-Carlo Simulation in Modeling for Hierarchical Linear Mixed Models -- 14. Monte-Carlo Simulation of Correlated Binary Responses -- 15. Monte Carlo Methods in Financial Modeling -- 16. Bayesian Intensive Computations in Elliptical Models.
520 $a This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications. It is divided into three parts, with the first providing an overview of Monte-Carlo techniques, the second focusing on missing data Monte-Carlo methods, and the third addressing Bayesian and general statistical modeling using Monte-Carlo simulations. The data and computer programs used here will also be made publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, and to readily apply them in their own research. Featuring highly topical content, the book has the potential to impact model development and data analyses across a wide spectrum of fields, and to spark further research in this direction.
650 0 $a Monte Carlo method. $3 186286
650 0 $a Mathematical statistics. $3 181877
650 1 4 $a Statistics. $3 182057
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 274067
650 2 4 $a Biostatistics. $3 339693
700 1 $a Chen, Ding-Geng $c (Din) $3 774264
700 1 $a Chen, John Dean. $3 774265
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a ICSA book series in statistics. $3 725077
856 4 0 $u http://dx.doi.org/10.1007/978-981-10-3307-0
950 $a Mathematics and Statistics (Springer-11649)