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Testing and Merging Information for Effect Size Estimation

Author

Listed:
  • Noriah Al-Kandari
  • Sana Buhamra
  • S. E. Ahmed

Abstract

A large-sample test for testing the equality of two effect sizes is presented. The null and non-null distributions of the proposed test statistic are derived. Further, the problem of estimating the effect size is considered when it is a priori suspected that two effect sizes may be close to each other. The combined data from all the samples leads to more efficient estimator of the effect size. We propose a basis for optimally combining estimation problems when there is uncertainty concerning the appropriate statistical model-estimator to use in representing the sampling process. The objective here is to produce natural adaptive estimators with some good statistical properties. In the context of two bivariate statistical models, the expressions for the asymptotic mean squared error of the proposed estimators are derived and compared with the parallel expressions for the benchmark estimators. We demonstrate that the suggested preliminary test estimator has superior asymptotic mean squared error performance relative to the benchmark and pooled estimators. A simulation study and application of the methodology to real data are presented.

Suggested Citation

  • Noriah Al-Kandari & Sana Buhamra & S. E. Ahmed, 2007. "Testing and Merging Information for Effect Size Estimation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(1), pages 47-60.
    • Handle: RePEc:taf:japsta:v:34:y:2007:i:1:p:47-60
    DOI: 10.1080/02664760600994604
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    References listed on IDEAS

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    1. BuHamra, Sana S. & Al-Kandari, N.M.Noriah M. & Ahmed, S. E., 2004. "Inference concerning quantile for left truncated and right censored data," Computational Statistics & Data Analysis, Elsevier, vol. 46(4), pages 819-831, July.
    2. Giles, Judith A & Giles, David E A, 1993. "Pre-test Estimation and Testing in Econometrics: Recent Developments," Journal of Economic Surveys, Wiley Blackwell, vol. 7(2), pages 145-197, June.
    3. Reif, Jiri & Vlcek, Karel, 2002. "Optimal pre-test estimators in regression," Journal of Econometrics, Elsevier, vol. 110(1), pages 91-102, September.
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