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Simultaneous confidence intervals for comparing several exponential location parameters with a control

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  • Parminder Singh
  • Anju Goyal
  • Amar Gill

Abstract

In this paper, two-sided simultaneous confidence intervals, on the lines of Hayter et al. (J Stat Plan Inference 86:81–99, 2000 ), to compare $$k$$ k two-parameter exponential populations with a control population in terms of location parameters are proposed, which combine the advantages of one-sided simultaneous confidence intervals and two-sided simultaneous confidence intervals of Bofinger (Aust J Stat 34(1):65–75, 1992 ). The proposed two-sided simultaneous confidence intervals also maintain the inferential sensitivity of positive directional decision of one-sided simultaneous confidence intervals. Computation of the critical constants of the proposed procedure is discussed and selected critical constants are tabulated. Working and advantages of the proposed procedure are demonstrated with a numerical example. Copyright Sapienza Università di Roma 2015

Suggested Citation

  • Parminder Singh & Anju Goyal & Amar Gill, 2015. "Simultaneous confidence intervals for comparing several exponential location parameters with a control," METRON, Springer;Sapienza Università di Roma, vol. 73(1), pages 99-118, April.
    • Handle: RePEc:spr:metron:v:73:y:2015:i:1:p:99-118
    DOI: 10.1007/s40300-014-0054-z
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    References listed on IDEAS

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    1. Parminder Singh & Asheber Abebe, 2009. "Comparing several exponential populations with more than one control," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(3), pages 359-374, August.
    2. Wu, Shu-Fei & Chen, Hubert J., 1998. "Multiple comparison procedures with the average for exponential location parameters," Computational Statistics & Data Analysis, Elsevier, vol. 26(4), pages 461-484, February.
    3. Wu, Shu-Fei & Lin, Ying-Po & Yu, Yuh-Ru, 2010. "One-stage multiple comparisons with the control for exponential location parameters under heteroscedasticity," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1372-1380, May.
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