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Inferences on the common mean of several inverse Gaussian populations

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  • Ye, Ren-Dao
  • Ma, Tie-Feng
  • Wang, Song-Gui

Abstract

This paper presents procedures for hypothesis testing and interval estimation for the common mean of several inverse Gaussian populations when the scalar parameters are unknown and unequal. The proposed approaches are hybrids between the generalized inference method and the large-sample theory. Some simulation results are presented to compare the performance of the proposed approaches with that of the existing approach. The simulation results indicate that one of the proposed approaches performs better than the existing approach in most cases. Furthermore, our approaches can be simply carried out by a few simulation steps. Finally, the proposed approaches are illustrated by using three examples.

Suggested Citation

  • Ye, Ren-Dao & Ma, Tie-Feng & Wang, Song-Gui, 2010. "Inferences on the common mean of several inverse Gaussian populations," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 906-915, April.
  • Handle: RePEc:eee:csdana:v:54:y:2010:i:4:p:906-915
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    References listed on IDEAS

    as
    1. Tian, Lili, 2006. "Testing equality of inverse Gaussian means under heterogeneity, based on generalized test variable," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1156-1162, November.
    2. K. Krishnamoorthy & Yong Lu, 2003. "Inferences on the Common Mean of Several Normal Populations Based on the Generalized Variable Method," Biometrics, The International Biometric Society, vol. 59(2), pages 237-247, June.
    3. Ho, Yu-Yun & Weerahandi, Sam, 2007. "Analysis of repeated measures under unequal variances," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 493-504, March.
    4. Zhou, L. P. & Mathew, T., 1994. "Combining Independent Tests in Multivariate Linear Models," Journal of Multivariate Analysis, Elsevier, vol. 51(2), pages 265-276, November.
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    Cited by:

    1. Mondal, Anjana & Kumar, Somesh, 2024. "Inference on order restricted means of inverse Gaussian populations under heteroscedasticity," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).
    2. Cuizhen Niu & Xu Guo & Wangli Xu & Lixing Zhu, 2014. "Testing equality of shape parameters in several inverse Gaussian populations," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(6), pages 795-809, August.
    3. 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.
    4. Tiefeng Ma & Shuangzhe Liu & S. Ahmed, 2014. "Shrinkage estimation for the mean of the inverse Gaussian population," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(6), pages 733-752, August.
    5. Liu, Xuhua & Li, Na & Hu, Yuqin, 2015. "Combining inferences on the common mean of several inverse Gaussian distributions based on confidence distribution," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 136-142.
    6. Shi, Jian-Hong & Lv, Jiang-Long, 2012. "A new generalized p-value for testing equality of inverse Gaussian means under heterogeneity," Statistics & Probability Letters, Elsevier, vol. 82(1), pages 96-102.
    7. Liwen Xu & Hongxia Guo & Shenghua Yu, 2018. "Generalized p value tests for variance components in a class of linear mixed models," Statistical Papers, Springer, vol. 59(2), pages 581-604, June.

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