Monte-Carlo Simulation-Based Statistical Modeling

Front Cover
Ding-Geng (Din) Chen, John Dean Chen
Springer, Feb 1, 2017 - Medical - 430 pages
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.

What people are saying - Write a review

We haven't found any reviews in the usual places.


Joint Generation of Binary Ordinal Count and Normal Data with Specified Marginal and Association Structures in MonteCarlo Simulations
Improving the Efficiency of the MonteCarlo Methods Using Ranked Simulated Approach
Normal and Nonnormal Data Simulations for the Evaluation of TwoSample Location Tests
Anatomy of Correlational Magnitude Transformations in Latency and Discretization Contexts in MonteCarlo Studies
MonteCarlo Simulation of Correlated Binary Responses
Quantifying the Uncertainty in Optimal Experiment Schemes via MonteCarlo Simulations
Part II MonteCarlo Methods in Missing Data
Markov Chain MonteCarlo Methods for Missing Data Under Ignorability Assumptions
Application of Markov Chain MonteCarlo Multiple Imputation Method to Deal with Missing Data from the Mechanism of MNAR in Sensitivity Anal...
Part III MonteCarlo in Statistical Modellings and Applications
MonteCarlo Simulation in Modeling for Hierarchical Generalized Linear Mixed Models
MonteCarlo Methods in Financial Modeling
Simulation Studies on the Effects of the Censoring Distribution Assumption in the Analysis of IntervalCensored Failure Time Data
Robust Bayesian Hierarchical Model Using MonteCarlo Simulation
A Comparison of Bootstrap Confidence Intervals for Multilevel Longitudinal Data Using MonteCarlo Simulation
BootstrapBased LASSOType Selection to Build Generalized Additive Partially Linear Models for HighDimensional Data

A MonteCarlo Technique
Hybrid MonteCarlo in Multiple Missing Data Imputations with Application to a Bone Fracture Data
Statistical Methodologies for Dealing with Incomplete Longitudinal Outcomes Due to Dropout Missing at Random
Applications of Simulation for Missing Data Issues in Longitudinal Clinical Trials
MonteCarlo SimulationBased Statistical Modeling

Other editions - View all

Common terms and phrases

About the author (2017)

Professor Ding-Geng Chen is a fellow of the American Statistical Association and currently the Wallace Kuralt distinguished professor at the University of North Carolina at Chapel Hill. He was a professor at the University of Rochester and the Karl E. Peace endowed eminent scholar chair in biostatistics at Georgia Southern University. He is also a senior statistics consultant for biopharmaceuticals and government agencies with extensive expertise in clinical trial biostatistics and public health statistics. Professor Chen has written more than 150 referred professional publications and co-authored and co-edited eight books on clinical trial methodology, meta-analysis, causal-inference and public health statistics.

mr. john="" dean="" chenbMr. John Dean Chen

Bibliographic information