Monte-Carlo Simulation-Based Statistical Modeling

<div>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.&nbsp;Quantifying the Uncertainty in Optimal Experimental Schemes via Monte-Carlo Simulations.- 5.&nbsp;Normal and Non-normal Data Simulations for the Evaluation of Two-sample Location Tests.- 6.&nbsp;Understanding dichotomization from Monte-Carlo Simulations.- Part 2: Monte-Carlo Methods in Missing Data.- 7.&nbsp;Hybrid Monte-Carlo in Multiple Missing Data Imputations with Application to a Bone Fracture Data.- 8.&nbsp;Methods for Handling Incomplete Longitudinal Data due to Missing at Random Dropout.- 9.&nbsp;Applications of Simulation for Missing Data Issues in Longitudinal Clinical Trials.- 10.&nbsp;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.&nbsp;Markov-Chain Monte-Carlo Methods in Statistical modelling.- 13.&nbsp;Monte-Carlo Simulation in Modeling for Hierarchical Linear Mixed Models.- 14.&nbsp;Monte-Carlo Simulation of Correlated Binary Responses.- 15.&nbsp;Monte Carlo Methods in Financial Modeling.- 16.&nbsp;Bayesian Intensive Computations in Elliptical Models.&nbsp;</div><div><br></div><div><br></div><div><br></div>