Monte-Carlo Simulation-Based Statistical Modeling

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 Chen is specialized in Monte-Carlo simulations in modelling financial market risk. In his career on Wall Street, he worked in Market Risk in commodities trading, structuring notes on the Exotics Interest Rate Derivatives desk at Barclays Capital. During his career in the financial industry, he witnessed in person the unfolding of the financial crisis, and the immediate aftermath consuming much of the financial industry. In its wake, a dizzying array of regulations were made from the government, severely limiting the businesses that once made banks so profitable. Mr Chen transitioned back to the Risk side of the business working in Market and Model Risk. He is currently a Vice President at Credit Suisse specializing in regulatory stress testing with  Monte-Carlo simulations. He graduated from the University of Washington with a dual Bachelors of Science in Applied Mathematics and Economics.  

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.