Monte-Carlo Simulation-Based Statistical Modeling

  • Ding-Geng (Din) Chen
  • John Dean Chen

Part of the ICSA Book Series in Statistics book series (ICSABSS)

Table of contents

  1. Front Matter
    Pages i-xx
  2. Monte-Carlo Techniques

  3. Monte-Carlo Methods in Missing Data

  4. Monte-Carlo in Statistical Modellings and Applications

  5. Ding-Geng (Din) Chen, John Dean Chen
    Pages E1-E1
  6. Back Matter
    Pages 425-430

About this book


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.


Monte-Carlo Techniques Statistical Modelling Importance Sampling Multiple Integration Simulation Efficiency Ranked Simulated Approach Life-testing Experiments

Editors and affiliations

  • Ding-Geng (Din) Chen
    • 1
  • John Dean Chen
    • 2
  1. 1.Gillings School of Global Public HealthUniversity of North CarolinaChapel HillUSA
  2. 2.Risk ManagementCredit Suisse Risk ManagementNew YorkUSA

Bibliographic information