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simsum: Analyses of simulation studies including Monte Carlo error

Author

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  • Ian R. White

    (MRC Biostatistics Unit)

Abstract

A new Stata command, simsum, analyzes data from simulation studies. The data may comprise point estimates and standard errors from several analysis methods, possibly resulting from several different simulation settings. simsum can report bias, coverage, power, empirical standard error, relative precision, average model-based standard error, and the relative error of the standard error. Monte Carlo errors are available for all of these estimated quantities. Copyright 2010 by StataCorp LP.

Suggested Citation

  • Ian R. White, 2010. "simsum: Analyses of simulation studies including Monte Carlo error," Stata Journal, StataCorp LP, vol. 10(3), pages 369-385, September.
    • Handle: RePEc:tsj:stataj:v:10:y:2010:i:3:p:369-385
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    References listed on IDEAS

    as
    1. Patrick Royston, 2004. "Multiple imputation of missing values," Stata Journal, StataCorp LP, vol. 4(3), pages 227-241, September.
    2. 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.
    3. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
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    Cited by:

    1. Tim Morris & Ella Marley-Zagar, 2023. "Coding robust simulation studies in Stata," Biostatistics and Epidemiology Virtual Symposium 2023 01, Stata Users Group.
    2. R. N. Rattihalli, 2023. "A Class of Multivariate Power Skew Symmetric Distributions: Properties and Inference for the Power-Parameter," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(2), pages 1356-1393, August.
    3. Edgar C. Merkle & Daniel Furr & Sophia Rabe-Hesketh, 2019. "Bayesian Comparison of Latent Variable Models: Conditional Versus Marginal Likelihoods," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 802-829, September.
    4. Warrington Nicole M. & Tilling Kate & Howe Laura D. & Paternoster Lavinia & Pennell Craig E. & Wu Yan Yan & Briollais Laurent, 2014. "Robustness of the linear mixed effects model to error distribution assumptions and the consequences for genome-wide association studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(5), pages 567-587, October.
    5. Ng'ombe, John, 2019. "Economics of the Greenseeder Hand Planter, Discrete Choice Modeling, and On-Farm Field Experimentation," Thesis Commons jckt7, Center for Open Science.
    6. Brian Gin & Nicholas Sim & Anders Skrondal & Sophia Rabe-Hesketh, 2020. "A Dyadic IRT Model," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 815-836, September.

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