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On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses


  • Koehler, Elizabeth
  • Brown, Elizabeth
  • Haneuse, Sebastien J.-P. A.


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Suggested Citation

  • Koehler, Elizabeth & Brown, Elizabeth & Haneuse, Sebastien J.-P. A., 2009. "On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses," The American Statistician, American Statistical Association, vol. 63(2), pages 155-162.
  • Handle: RePEc:bes:amstat:v:63:i:2:y:2009:p:155-162

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    2. Sun-Joo Cho & Paul Boeck & Susan Embretson & Sophia Rabe-Hesketh, 2014. "Additive Multilevel Item Structure Models with Random Residuals: Item Modeling for Explanation and Item Generation," Psychometrika, Springer;The Psychometric Society, vol. 79(1), pages 84-104, January.
    3. Michel Winter & Isabelle Mirbel & Pierre Crescenzo, 2014. "Modeling Uncertainty when Estimating IT Projects Costs," Working Papers hal-00966573, HAL.
    4. Dorinth van Dijk & David Geltner & Alex van de Minne, 2018. "Revisiting supply and demand indexes in real estate," DNB Working Papers 583, Netherlands Central Bank, Research Department.
    5. Ian R. White, 2010. "simsum: Analyses of simulation studies including Monte Carlo error," Stata Journal, StataCorp LP, vol. 10(3), pages 369-385, September.
    6. Vitoratou, Silia & Ntzoufras, Ioannis & Moustaki, Irini, 2016. "Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions," LSE Research Online Documents on Economics 57685, London School of Economics and Political Science, LSE Library.
    7. David A. Wagstaff & Ofer Harel, 2011. "A closer examination of three small-sample approximations to the multiple-imputation degrees of freedom," Stata Journal, StataCorp LP, vol. 11(3), pages 403-419, September.

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