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Parametric and nonparametric confidence intervals for estimating the difference of means of two skewed populations

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  • B. M. Golam Kibria
  • Shipra Banik

Abstract

In this paper, we have reviewed and proposed several interval estimators for estimating the difference of means of two skewed populations. Estimators include the ordinary- t , two versions proposed by Welch [17] and Satterthwaite [15], three versions proposed by Zhou and Dinh [18], Johnson [9], Hall [8], empirical likelihood (EL), bootstrap version of EL, median t proposed by Baklizi and Kibria [2] and bootstrap version of median t . A Monte Carlo simulation study has been conducted to compare the performance of the proposed interval estimators. Some real life health related data have been considered to illustrate the application of the paper. Based on our findings, some possible good interval estimators for estimating the mean difference of two populations have been recommended for the researchers.

Suggested Citation

  • B. M. Golam Kibria & Shipra Banik, 2013. "Parametric and nonparametric confidence intervals for estimating the difference of means of two skewed populations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(12), pages 2617-2636, December.
  • Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2617-2636
    DOI: 10.1080/02664763.2013.822478
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    1. Ayman Baklizi & B.M. Golam Kibria, 2009. "One and two sample confidence intervals for estimating the mean of skewed populations: an empirical comparative study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(6), pages 601-609.
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    Cited by:

    1. N. Balakrishnan & N. Martín & L. Pardo, 2017. "Empirical phi-divergence test statistics for the difference of means of two populations," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(2), pages 199-226, April.

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    2. N. Balakrishnan & N. Martín & L. Pardo, 2017. "Empirical phi-divergence test statistics for the difference of means of two populations," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(2), pages 199-226, April.

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