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Analysis of covariance under inverse Gaussian model

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  • Mohammad Reza Meshkani
  • Afshin Fallah
  • Amir Kavousi

Abstract

This paper considers the problem of analysis of covariance (ANCOVA) under the assumption of inverse Gaussian distribution for response variable. We develop the essential methodology for estimating the model parameters via maximum likelihood method. The general form of the maximum likelihood estimator is obtained in color closed form. Adjusted treatment effects and adjusted covariate effects are given, too. We also provide the asymptotic distribution of the proposed estimators. A simulation study and a real world application are also performed to illustrate and evaluate the proposed methodology.

Suggested Citation

  • Mohammad Reza Meshkani & Afshin Fallah & Amir Kavousi, 2014. "Analysis of covariance under inverse Gaussian model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1189-1202, June.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1189-1202
    DOI: 10.1080/02664763.2013.862222
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    References listed on IDEAS

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    1. Kheiri, Soleiman & Kimber, Alan & Reza Meshkani, Mohammad, 2007. "Bayesian analysis of an inverse Gaussian correlated frailty model," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5317-5326, July.
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