Bayesian Estimation of The Ex-Gaussian Distribution

  • Abir El Haj University of Poitiers
  • Yousri Slaoui University of Poitiers
  • Clara Solier University of Poitiers
  • Cyril Perret University of Poitiers
Keywords: Adaptive rejection Metropolis sampling; Bayesian estimation approach; Exponential modified Gaussian distribution; Maximum likelihood estimation; Quantile maximum likelihood: Response time.


Fitting of the exponential modified Gaussian distribution to model reaction times and drawing conclusions from its estimated parameter values is one of the most popular method used in psychology. This paper aims to develop a Bayesian approach to estimate the parameters of the ex-Gaussian distribution. Since the chosen priors yield to posterior densities that are not of known form and that they are not always log-concave, we suggest to use the adaptive rejection Metropolis sampling method. Applications on simulated data and on real data are provided to compare this method to the standard maximum likelihood estimation method as well as the quantile maximum likelihood estimation. Results shows the effectiveness of the proposed Bayesian method by computing the root mean square error of the estimated parameters using the three methods.


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How to Cite
El Haj, A., Slaoui, Y., Solier, C., & Perret, C. (2021). Bayesian Estimation of The Ex-Gaussian Distribution. Statistics, Optimization & Information Computing, 9(4), 809-819.
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