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Simulation for Data Science with R

You're reading from   Simulation for Data Science with R Effective Data-driven Decision Making

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Product type Paperback
Published in Jun 2016
Publisher Packt
ISBN-13 9781785881169
Length 398 pages
Edition 1st Edition
Languages
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Author (1):
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Matthias Templ Matthias Templ
Author Profile Icon Matthias Templ
Matthias Templ
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Table of Contents (13) Chapters Close

Preface 1. Introduction 2. R and High-Performance Computing FREE CHAPTER 3. The Discrepancy between Pencil-Driven Theory and Data-Driven Computational Solutions 4. Simulation of Random Numbers 5. Monte Carlo Methods for Optimization Problems 6. Probability Theory Shown by Simulation 7. Resampling Methods 8. Applications of Resampling Methods and Monte Carlo Tests 9. The EM Algorithm 10. Simulation with Complex Data 11. System Dynamics and Agent-Based Models Index

Model-based simulation studies


As already mentioned, for some situations the formulation of and conduction of a precise mathematical treatment is often too difficult or too time-consuming. By using model-based simulation we may approximate real-world situations and results whenever the data is not sampled with a complex sampling design. Model-based simulation studies especially require much less time, effort, and/or money than a mathematical proof of properties of estimators or methods.

Latent model example continued

We will continue with the latent model from the previous example. Such datasets we may use for the comparison of methods. For example, one can mark values to be missing, impute them by suitable imputation methods and evaluate and compare the imputation methods. We can do this by example for a smaller dataset and compare mean imputation, nearest neighbor imputation, robust model-based imputation, and imputation by mice by using a simple precision-based error criterion based on...

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