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A generalized p-value approach for comparing the means of several log-normal populations

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  • Li, Xinmin

Abstract

For the problem of comparing the means of several log-normal populations, a novel approach based on a generalized p-value is given. The merits of the proposed method are numerically compared with the existing method with respect to their sizes and powers under different scenarios. The simulation results demonstrate that the proposed approach can perform hypothesis testing with satisfactory sizes.

Suggested Citation

  • Li, Xinmin, 2009. "A generalized p-value approach for comparing the means of several log-normal populations," Statistics & Probability Letters, Elsevier, vol. 79(11), pages 1404-1408, June.
    • Handle: RePEc:eee:stapro:v:79:y:2009:i:11:p:1404-1408
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    References listed on IDEAS

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    1. Gupta, Ramesh C. & Li, Xue, 2006. "Statistical inference for the common mean of two log-normal distributions and some applications in reliability," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3141-3164, July.
    2. Krishnamoorthy, K. & Lu, Fei & Mathew, Thomas, 2007. "A parametric bootstrap approach for ANOVA with unequal variances: Fixed and random models," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5731-5742, August.
    3. Jiin-Huarng Guo & Wei-Ming Luh, 2000. "Testing methods for the one-way fixed effects ANOVA models of log-normal samples," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(6), pages 731-738.
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    Cited by:

    1. Ayanendranath Basu & Abhijit Mandal & Nirian Martín & Leandro Pardo, 2019. "A Robust Wald-Type Test for Testing the Equality of Two Means from Log-Normal Samples," Methodology and Computing in Applied Probability, Springer, vol. 21(1), pages 85-107, March.
    2. Schaarschmidt, Frank, 2013. "Simultaneous confidence intervals for multiple comparisons among expected values of log-normal variables," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 265-275.
    3. Shin-Fu Tsai, 2019. "Comparing Coefficients Across Subpopulations in Gaussian Mixture Regression Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 610-633, December.
    4. Yin, Jingjing & Tian, Lili, 2014. "Joint inference about sensitivity and specificity at the optimal cut-off point associated with Youden index," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 1-13.
    5. Yongku Kim & Woo Dong Lee & Sang Gil Kang, 2020. "A matching prior based on the modified profile likelihood for the common mean in multiple log-normal distributions," Statistical Papers, Springer, vol. 61(2), pages 543-573, April.
    6. Ali Akbar Jafari & Javad Shaabani, 2020. "Comparing scale parameters in several gamma distributions with known shapes," Computational Statistics, Springer, vol. 35(4), pages 1927-1950, December.
    7. Chang, Ming & You, Xuqun & Wen, Muqing, 2012. "Testing the homogeneity of inverse Gaussian scale-like parameters," Statistics & Probability Letters, Elsevier, vol. 82(10), pages 1755-1760.
    8. Xu Guo & Hecheng Wu & Gaorong Li & Qiuyue Li, 2017. "Inference for the common mean of several Birnbaum–Saunders populations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(5), pages 941-954, April.
    9. Sadooghi-Alvandi, S.M. & Malekzadeh, A., 2014. "Simultaneous confidence intervals for ratios of means of several lognormal distributions: A parametric bootstrap approach," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 133-140.
    10. S. Lin, 2013. "The higher order likelihood method for the common mean of several log-normal distributions," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(3), pages 381-392, April.
    11. Li, Xinmin & Tian, Lili & Wang, Juan & Muindi, Josephia R., 2012. "Comparison of quantiles for several normal populations," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2129-2138.

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