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Assessing Variability of Complex Descriptive Statistics in Monte Carlo Studies Using Resampling Methods

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  • Dennis D. Boos
  • Jason A. Osborne

Abstract

type="main" xml:id="insr12087-abs-0001"> Good statistical practice dictates that summaries in Monte Carlo studies should always be accompanied by standard errors. Those standard errors are easy to provide for summaries that are sample means over the replications of the Monte Carlo output: for example, bias estimates, power estimates for tests and mean squared error estimates. But often more complex summaries are of interest: medians (often displayed in boxplots), sample variances, ratios of sample variances and non-normality measures such as skewness and kurtosis. In principle, standard errors for most of these latter summaries may be derived from the Delta Method, but that extra step is often a barrier for standard errors to be provided. Here, we highlight the simplicity of using the jackknife and bootstrap to compute these standard errors, even when the summaries are somewhat complicated. © 2014 The Authors. International Statistical Review © 2014 International Statistical Institute

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  • Dennis D. Boos & Jason A. Osborne, 2015. "Assessing Variability of Complex Descriptive Statistics in Monte Carlo Studies Using Resampling Methods," International Statistical Review, International Statistical Institute, vol. 83(2), pages 228-238, August.
    • Handle: RePEc:bla:istatr:v:83:y:2015:i:2:p:228-238
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    File URL: http://hdl.handle.net/10.1111/insr.12087
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    References listed on IDEAS

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    1. Bao, Yong, 2009. "Finite-Sample Moments Of The Coefficient Of Variation," Econometric Theory, Cambridge University Press, vol. 25(1), pages 291-297, February.
    2. Jay M. Ver Hoef, 2012. "Who Invented the Delta Method?," The American Statistician, Taylor & Francis Journals, vol. 66(2), pages 124-127, May.
    3. Tobias Niebuhr & Jens-Peter Kreiss, 2014. "Asymptotics for Autocovariances and Integrated Periodograms for Linear Processes Observed at Lower Frequencies," International Statistical Review, International Statistical Institute, vol. 82(1), pages 123-140, April.
    4. Hong Zhu, 2014. "Non-parametric Analysis of Gap Times for Multiple Event Data: An Overview," International Statistical Review, International Statistical Institute, vol. 82(1), pages 106-122, April.
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