Monte-Carlo Simulation-Based Statistical Modeling

✍ Scribed by Ding-Geng (Din) Chen, John Dean Chen (eds.)

• Publisher: Springer Singapore
• Year: 2017
• Tongue: English
• Leaves: 438
• Series: ICSA Book Series in Statistics
• Edition: 1
• Category: Library

✦ Synopsis

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

Front Matter....Pages i-xviii
Front Matter....Pages 1-1
Joint Generation of Binary, Ordinal, Count, and Normal Data with Specified Marginal and Association Structures in Monte-Carlo Simulations....Pages 3-15
Improving the Efficiency of the Monte-Carlo Methods Using Ranked Simulated Approach....Pages 17-40
Normal and Non-normal Data Simulations for the Evaluation of Two-Sample Location Tests....Pages 41-57
Anatomy of Correlational Magnitude Transformations in Latency and Discretization Contexts in Monte-Carlo Studies....Pages 59-84
Monte-Carlo Simulation of Correlated Binary Responses....Pages 85-105
Quantifying the Uncertainty in Optimal Experiment Schemes via Monte-Carlo Simulations....Pages 107-126
Front Matter....Pages 127-127
Markov Chain Monte-Carlo Methods for Missing Data Under Ignorability Assumptions....Pages 129-142
A Multiple Imputation Framework for Massive Multivariate Data of Different Variable Types: A Monte-Carlo Technique....Pages 143-162
Hybrid Monte-Carlo in Multiple Missing Data Imputations with Application to a Bone Fracture Data....Pages 163-178
Statistical Methodologies for Dealing with Incomplete Longitudinal Outcomes Due to Dropout Missing at Random....Pages 179-209
Applications of Simulation for Missing Data Issues in Longitudinal Clinical Trials....Pages 211-232
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....Pages 233-252
Front Matter....Pages 253-253
Monte-Carlo Simulation in Modeling for Hierarchical Generalized Linear Mixed Models....Pages 255-283
Monte-Carlo Methods in Financial Modeling....Pages 285-317
Simulation Studies on the Effects of the Censoring Distribution Assumption in the Analysis of Interval-Censored Failure Time Data....Pages 319-346
Robust Bayesian Hierarchical Model Using Monte-Carlo Simulation....Pages 347-366
A Comparison of Bootstrap Confidence Intervals for Multi-level Longitudinal Data Using Monte-Carlo Simulation....Pages 367-403
Bootstrap-Based LASSO-Type Selection to Build Generalized Additive Partially Linear Models for High-Dimensional Data....Pages 405-424
Back Matter....Pages 425-430

✦ Subjects

Statistics for Life Sciences, Medicine, Health Sciences;Biostatistics