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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

Design-based simulation


Design-based simulations are particularly important when the selection probabilities for statistical units of a finite sampling frame are not equal, that is, when samples are drawn with a complex sampling design. This primarily relates to any sampling from finite populations, for example, samples drawn from a population register.

The costs of a sample survey can be reduced if the sample is drawn with a certain complex sampling design. For example, for poverty measurement, a household with a single parent and children might be included with a higher probability than households with another composition of household members, because it's likely that the single parent household is poor (basically the target variable).

Tip

Basically, in design-based simulations R samples from a finite population are drawn using a complex sampling design, wherein the population is simulated in a close-to-reality manner.

For each sample, a parameter of the population is estimated and the estimations...

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