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Monte-Carlo Simulation-Based Statistical Modeling

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Cover of 'Monte-Carlo Simulation-Based Statistical Modeling'

Table of Contents

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    Book Overview
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    Chapter 1 Joint Generation of Binary, Ordinal, Count, and Normal Data with Specified Marginal and Association Structures in Monte-Carlo Simulations
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    Chapter 2 Improving the Efficiency of the Monte-Carlo Methods Using Ranked Simulated Approach
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    Chapter 3 Normal and Non-normal Data Simulations for the Evaluation of Two-Sample Location Tests
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    Chapter 4 Anatomy of Correlational Magnitude Transformations in Latency and Discretization Contexts in Monte-Carlo Studies
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    Chapter 5 Monte-Carlo Simulation of Correlated Binary Responses
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    Chapter 6 Quantifying the Uncertainty in Optimal Experiment Schemes via Monte-Carlo Simulations
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    Chapter 7 Markov Chain Monte-Carlo Methods for Missing Data Under Ignorability Assumptions
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    Chapter 8 A Multiple Imputation Framework for Massive Multivariate Data of Different Variable Types: A Monte-Carlo Technique
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    Chapter 9 Hybrid Monte-Carlo in Multiple Missing Data Imputations with Application to a Bone Fracture Data
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    Chapter 10 Statistical Methodologies for Dealing with Incomplete Longitudinal Outcomes Due to Dropout Missing at Random
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    Chapter 11 Applications of Simulation for Missing Data Issues in Longitudinal Clinical Trials
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    Chapter 12 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
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    Chapter 13 Monte-Carlo Simulation in Modeling for Hierarchical Generalized Linear Mixed Models
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    Chapter 14 Monte-Carlo Methods in Financial Modeling
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    Chapter 15 Simulation Studies on the Effects of the Censoring Distribution Assumption in the Analysis of Interval-Censored Failure Time Data
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    Chapter 16 Robust Bayesian Hierarchical Model Using Monte-Carlo Simulation
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    Chapter 17 A Comparison of Bootstrap Confidence Intervals for Multi-level Longitudinal Data Using Monte-Carlo Simulation
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    Chapter 18 Bootstrap-Based LASSO-Type Selection to Build Generalized Additive Partially Linear Models for High-Dimensional Data
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    Chapter 19 Erratum to: Monte-Carlo Simulation-Based Statistical Modeling
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Title
Monte-Carlo Simulation-Based Statistical Modeling
Published by
Springer Singapore, January 2017
DOI 10.1007/978-981-10-3307-0
ISBNs
978-9-81-103306-3, 978-9-81-103307-0
Editors

Ding-Geng (Din) Chen, John Dean Chen

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 4%
Student > Master 1 4%
Unknown 26 93%
Readers by discipline Count As %
Engineering 1 4%
Unknown 27 96%