モンテカルロ・シミュレーションにもとづく統計モデル化Monte-Carlo Simulation-Based Statistical Modeling
（ICSA Book Series in Statistics）

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モンテカルロ・シミュレーションにもとづく統計モデル化Monte-Carlo Simulation-Based Statistical Modeling
（ICSA Book Series in Statistics）

Chen, Ding-Geng (Din) (EDT)／Chen, John Dean (EDT)

Springer 2017/02
テキストの言語: ENG 出版国: SG
ISBN： 9789811033063
KNPID： EY00151702

販売価格￥21,777（本体 ￥19,798）　

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DDC:
570.1
KDC:
F16 確率・統計

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Full Description

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.

Table of Contents

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. 

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