Monte-Carlo simulation-based statistical modeling. (2017)

• Record Type: Book
• Further Information: Note: Ding-Geng (Din) Chen, John Dean Chen, editors.
• Editors: Chen, Ding-Geng; Chen, John Dean
• Contents: 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.
• Publisher Details: Singapore : Springer
• Publication Date: 2017
• Extent: 1 online resource
• Subjects: 518/.282; Statistics; Monte Carlo method; Mathematical statistics; MATHEMATICS -- Numerical Analysis; Mathematical statistics; Monte Carlo method; Statistics; Statistics for Life Sciences, Medicine, Health Sciences; Biostatistics; Science -- Life Sciences -- General; Life sciences: general issues; Statistical methods; Medical -- Biostatistics; Probability & statistics; Electronic books
• Languages: English
• ISBNs: 9789811033070; 9811033072
• Related ISBNs: 9789811033063; 9811033064
• Notes: Note: Includes bibliographical references and index.; Note: Online resource; title from PDF title page (SpringerLink, viewed February 9, 2017).
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