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Preface; Part I: Monte-Carlo Techniques (Chapters "Joint Generation of Binary, Ordinal, Count, and Normal Data with Specified Marginal and Association Structures in Monte-Carlo Simulations"-"Quantifying the Uncertainty in Optimal Experiment Schemes via Monte-Carlo Simulations"); Part II: Monte-Carlo Methods for Missing Data (Chapters "Markov Chain Monte-Carlo Methods for Missing Data Under Ignorability Assumptions"-"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")
Part III: Monte-Carlo in Statistical Modellings and Applications (Chapters "Monte-Carlo Simulation in Modeling for Hierarchical Generalized Linear Mixed Models"-"Bootstrap-Based LASSO-type Selection to Build Generalized Additive Partially Linear Models for High-Dimensional Data")About the Book; Contents; Editors and Contributors; Part I Monte-Carlo Techniques; Joint Generation of Binary, Ordinal, Count, and Normal Data with Specified Marginal and Association Structures in Monte-Carlo Simulations; 1 Introduction; 2 Algorithm; 3 Some Operational Details and an Illustrative Example
4 Future DirectionsReferences; Improving the Efficiency of the Monte-Carlo Methods Using Ranked Simulated Approach; 1 Introduction; 2 Steady-State Ranked Simulated Sampling (SRSIS); 3 Monte-Carlo Methods for Multiple Integration Problems; 3.1 Importance Sampling Method; 3.2 Using Bivariate Steady-State Sampling (BVSRSIS); 3.3 Simulation Study; 4 Steady-State Ranked Gibbs Sampler; 4.1 Traditional (standard) Gibbs Sampling Method; 4.2 Steady-State Gibbs Sampling (SSGS): The Proposed Algorithms; 4.3 Simulation Study and Illustrations; References
Normal and Non-normal Data Simulations for the Evaluation of Two-Sample Location Tests1 Introduction; 2 Statistical Tests; 2.1 t-Test; 2.2 Wilcoxon Rank-Sum Test; 2.3 Two-Stage Test; 2.4 Permutation Test; 3 Simulations; 4 Results; 4.1 Heterogeneous Variance; 4.2 Skewness; 4.3 Kurtosis; 5 Discussion; References; Anatomy of Correlational Magnitude Transformations in Latency and Discretization Contexts in Monte-Carlo Studies; 1 Introduction; 2 Building Blocks; 2.1 Dichotomous Case: Normality; 2.2 Dichotomous Case: Beyond Normality; 2.3 Polytomous Case: Normality
2.4 Polytomous Case: Beyond Normality3 Algorithms and Illustrative Examples; 4 Simulations in a Multivariate Setting; 5 Discussion; References; Monte-Carlo Simulation of Correlated Binary Responses; 1 Introduction; 1.1 Binary Data Issues; 2 Fully Specified Joint Probability Distributions; 2.1 Simulating Binary Data with a Joint PDF; 2.2 Explicit Specification of the Joint PDF; 2.3 Derivation of the Joint PDF; 3 Specification by Mixture Distributions; 3.1 Mixtures Involving Discrete Distributions; 3.2 Mixtures Involving Continuous Distributions; 4 Simulation by Dichotomizing Variates
Part III: Monte-Carlo in Statistical Modellings and Applications (Chapters "Monte-Carlo Simulation in Modeling for Hierarchical Generalized Linear Mixed Models"-"Bootstrap-Based LASSO-type Selection to Build Generalized Additive Partially Linear Models for High-Dimensional Data")About the Book; Contents; Editors and Contributors; Part I Monte-Carlo Techniques; Joint Generation of Binary, Ordinal, Count, and Normal Data with Specified Marginal and Association Structures in Monte-Carlo Simulations; 1 Introduction; 2 Algorithm; 3 Some Operational Details and an Illustrative Example
4 Future DirectionsReferences; Improving the Efficiency of the Monte-Carlo Methods Using Ranked Simulated Approach; 1 Introduction; 2 Steady-State Ranked Simulated Sampling (SRSIS); 3 Monte-Carlo Methods for Multiple Integration Problems; 3.1 Importance Sampling Method; 3.2 Using Bivariate Steady-State Sampling (BVSRSIS); 3.3 Simulation Study; 4 Steady-State Ranked Gibbs Sampler; 4.1 Traditional (standard) Gibbs Sampling Method; 4.2 Steady-State Gibbs Sampling (SSGS): The Proposed Algorithms; 4.3 Simulation Study and Illustrations; References
Normal and Non-normal Data Simulations for the Evaluation of Two-Sample Location Tests1 Introduction; 2 Statistical Tests; 2.1 t-Test; 2.2 Wilcoxon Rank-Sum Test; 2.3 Two-Stage Test; 2.4 Permutation Test; 3 Simulations; 4 Results; 4.1 Heterogeneous Variance; 4.2 Skewness; 4.3 Kurtosis; 5 Discussion; References; Anatomy of Correlational Magnitude Transformations in Latency and Discretization Contexts in Monte-Carlo Studies; 1 Introduction; 2 Building Blocks; 2.1 Dichotomous Case: Normality; 2.2 Dichotomous Case: Beyond Normality; 2.3 Polytomous Case: Normality
2.4 Polytomous Case: Beyond Normality3 Algorithms and Illustrative Examples; 4 Simulations in a Multivariate Setting; 5 Discussion; References; Monte-Carlo Simulation of Correlated Binary Responses; 1 Introduction; 1.1 Binary Data Issues; 2 Fully Specified Joint Probability Distributions; 2.1 Simulating Binary Data with a Joint PDF; 2.2 Explicit Specification of the Joint PDF; 2.3 Derivation of the Joint PDF; 3 Specification by Mixture Distributions; 3.1 Mixtures Involving Discrete Distributions; 3.2 Mixtures Involving Continuous Distributions; 4 Simulation by Dichotomizing Variates