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Monte Carlo Statistical Methods pp 123–156Cite as

Controling Monte Carlo Variance

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Abstract

In Chapter 3, the Monte Carlo method was introduced (and discussed) as a simulation-based approach to the approximation of complex integrals. There has been a considerable body of work in this area and, while not all of it is completely relevant for this book, in this chapter we discuss the specifics of variance estimation and control. These are fundamental concepts, and we will see connections with similar developments in the realm of MCMC algorithms that are discussed in Chapters 7–12.

The others regarded him uncertainly, none of them sure how he had arrived at such a conclusion or on how to refute it.

—Susanna Gregory, A Deadly Brew

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Notes

  • McKay, M., Beckman, R., and Conover, W. (1979). A comparison of three methods for selecting values of output variables in the analysis of output from a computer code. Technometrics, 21: 239–245.

    MathSciNet  MATH  Google Scholar 

  • Mead, R. (1988). The Design of Experiments. Cambridge University Press, Cambridge.

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  • Kuehl, R. (1994). Statistical Principles of Research Design and Analysis. Duxbury, Belmont.

    MATH  Google Scholar 

  • Loh, W. (1996). On latin hypercube sampling. Ann. Statist., 24: 2058–2080.

    Article  MathSciNet  MATH  Google Scholar 

  • Stein, M. (1987). Large sample properties of simulations using latin hypercube sampling. Technometrics, 29: 143–151.

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. CEREMADE, Université Paris Dauphine, 75775, Paris Cedex 16, France

    Christian P. Robert

  2. Department of Statistics, University of Florida, 32611-8545, Gainesville, FL, USA

    George Casella

Authors
  1. Christian P. Robert
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  2. George Casella
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© 2004 Springer Science+Business Media New York

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Robert, C.P., Casella, G. (2004). Controling Monte Carlo Variance. In: Monte Carlo Statistical Methods. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-4145-2_4

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  • DOI: https://doi.org/10.1007/978-1-4757-4145-2_4

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4419-1939-7

  • Online ISBN: 978-1-4757-4145-2

  • eBook Packages: Springer Book Archive

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