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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

References


  • Alfons, A. 2012. CvTools: Cross-Validation Tools for Regression Models. https://CRAN.R-project.org/package=cvTools.

  • Chernick, M. R. 1999. Bootstrap Methods: A Practitioner's Guide. New York, NY: John Wiley.

  • Davison, A. C., and D. V. Hinkley. 1997. Bootstrap Methods and Their Application. Cambridge: Cambridge University Press.

  • Efron, B. 1987. "Better Bootstrap Confidence Intervals." Journal of the American Statistical Association 82: 171–85.

  • Efron, B. 1992. "Jackknife-After-Bootstrap Standard Errors and Influence Functions (with Discussion)." Journal of the Royal Statistical Society B 54: 83–127.

  • Efron, B., and R. J. Tibshirani. 1993. An Introduction to the Bootstrap. New York, NY: Chapman & Hall.

  • Freeman, J. 2009. "Bootstraps and Baron Munchhausen." Boston.com.

  • Good, P. 1993. Permutation Tests. New York: Springer Verlag.

  • Hesterberg, T.C. 2015. „What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum." The American Statistician 69 (4): 371...

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