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Statistical simulation based on right skewed distributions

Author

Listed:
  • Kazushi Maruo

    (Kowa Company, LTD.
    National Center of Neurology and Psychiatry)

  • Takaharu Yamabe

    (GPD Statistics, Pfizer Inc.)

  • Yusuke Yamaguchi

    (Astellas Pharma Inc.)

Abstract

Statistical simulations in medical and biological research are usually conducted with normal random numbers. However, in many cases, the distributions of real data in medical fields are usually right skewed. The conclusions led by simulations with the misspecified model might be misleading because of a gap between real data’s distribution and theoretical one. In this paper, we provide the simulation procedure for right skewed data based on reparameterized, easily interpretable parameters of the Box–Cox transformation model which includes multivariate distributions and regression models. We also show that the provided procedure is widely applicable to real world based on laboratory data, and then we provide parameter vector sets obtained by reparameterized parameter estimates that would cover almost all situations in which the distributions of data were right skewed and unimodal.

Suggested Citation

  • Kazushi Maruo & Takaharu Yamabe & Yusuke Yamaguchi, 2017. "Statistical simulation based on right skewed distributions," Computational Statistics, Springer, vol. 32(3), pages 889-907, September.
    • Handle: RePEc:spr:compst:v:32:y:2017:i:3:d:10.1007_s00180-016-0664-4
    DOI: 10.1007/s00180-016-0664-4
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    References listed on IDEAS

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    1. Matthew J. Gurka & Lloyd J. Edwards & Keith E. Muller & Lawrence L. Kupper, 2006. "Extending the Box–Cox transformation to the linear mixed model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(2), pages 273-288, March.
    2. Joe, Harry, 2005. "Asymptotic efficiency of the two-stage estimation method for copula-based models," Journal of Multivariate Analysis, Elsevier, vol. 94(2), pages 401-419, June.
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    Cited by:

    1. Wen Zhang & Zhibin Wu, 2022. "Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 615-632, April.

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