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Stochastic analysis of covariance when the error distribution is long-tailed symmetric

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  • Pelin Kasap
  • Birdal Senoglu
  • Olcay Arslan

Abstract

In this study, we consider stochastic one-way analysis of covariance model when the distribution of the error terms is long-tailed symmetric. Estimators of the unknown model parameters are obtained by using the maximum likelihood (ML) methodology. Iteratively reweighting algorithm is used to compute the ML estimates of the parameters. We also propose new test statistic based on ML estimators for testing the linear contrasts of the treatment effects. In the simulation study, we compare the efficiencies of the traditional least-squares (LS) estimators of the model parameters with the corresponding ML estimators. We also compare the power of the test statistics based on LS and ML estimators, respectively. A real-life example is given at the end of the study.

Suggested Citation

  • Pelin Kasap & Birdal Senoglu & Olcay Arslan, 2016. "Stochastic analysis of covariance when the error distribution is long-tailed symmetric," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(11), pages 1977-1997, August.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:11:p:1977-1997
    DOI: 10.1080/02664763.2015.1125866
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    References listed on IDEAS

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    1. Tiku, Moti L. & Islam, M. Qamarul & Sazak, Hakan S., 2008. "Estimation in bivariate nonnormal distributions with stochastic variance functions," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1728-1745, January.
    2. Bowman, K. O. & Shenton, L. R., 2001. "Weibull distributions when the shape parameter is defined," Computational Statistics & Data Analysis, Elsevier, vol. 36(3), pages 299-310, May.
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