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Bayesian analysis of covariance under inverse Gaussian model

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  • Mohammadreza 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 from the Bayesian point of view. We develop a fully Bayesian model for ANCOVA based on the conjugate prior distributions for parameters contained in the model. The Bayes estimator of parameters, ANCOVA model and adjusted effects for both treatments and covariates along with predictive distribution of future observations are developed. We also provide the essentials for comparing adjusted treatments effects and adjusted factor effects. A simulation study and a real world application are also performed to illustrate and evaluate the proposed Bayesian model.

Suggested Citation

  • Mohammadreza Meshkani & Afshin Fallah & Amir Kavousi, 2016. "Bayesian analysis of covariance under inverse Gaussian model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(2), pages 280-298, February.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:2:p:280-298
    DOI: 10.1080/02664763.2015.1049131
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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.
    2. Ananda, Malwane M. A., 1998. "Bayesian and non-bayesian solutions to analysis of covariance models under heteroscedasticity," Journal of Econometrics, Elsevier, vol. 86(1), pages 177-192, June.
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