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The Use of Control Variates in Monte Carlo Estimation of Power

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  • P. Rothery

Abstract

The control variate technique for variance reduction is used in the estimation by computer simulation of the power of a non‐parametric test. Efficiency gains of as much as six were obtained for relatively small increases in computing time.

Suggested Citation

  • P. Rothery, 1982. "The Use of Control Variates in Monte Carlo Estimation of Power," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(2), pages 125-129, June.
    • Handle: RePEc:bla:jorssc:v:31:y:1982:i:2:p:125-129
    DOI: 10.2307/2347974
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    Cited by:

    1. Davidson, Russell & MacKinnon, James G., 1992. "Regression-based methods for using control variates in Monte Carlo experiments," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 203-222.
    2. Russell Davidson & James G. Mackinnon, 1990. "Regression-Based Methods for Using Control and Antithetic Variates in Monte Carlo Experiments," Working Paper 781, Economics Department, Queen's University.
    3. Ocana, Jordi & Vegas, Esteban, 1995. "Variance reduction for Bernoulli response variables in simulation," Computational Statistics & Data Analysis, Elsevier, vol. 19(6), pages 631-640, June.
    4. Timothy C. Hesterberg & Barry L. Nelson, 1998. "Control Variates for Probability and Quantile Estimation," Management Science, INFORMS, vol. 44(9), pages 1295-1312, September.
    5. Modarres, Reza, 2002. "Efficient nonparametric estimation of a distribution function," Computational Statistics & Data Analysis, Elsevier, vol. 39(1), pages 75-95, March.
    6. Neil R. Ericsson, 1987. "Monte Carlo methodology and the finite sample properties of statistics for testing nested and non-nested hypotheses," International Finance Discussion Papers 317, Board of Governors of the Federal Reserve System (U.S.).
    7. Dobbin, Kevin K. & Ionan, Alexei C., 2015. "Sample size methods for constructing confidence intervals for the intra-class correlation coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 85(C), pages 67-83.

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