Monte-Carlo Simulation-Based Statistical Modeling
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 overv...
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| Other Authors: | , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Singapore :
Springer Singapore : Imprint: Springer,
2017.
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| Series: | ICSA Book Series in Statistics,
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| Subjects: | |
| Online Access: | Full Text via HEAL-Link |
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. .
