A bayesian analysis of the annual maximum temperature using generalized extreme value distribution

Authors

  • CHERAITIA HASSEN

DOI:

https://doi.org/10.54302/mausam.v72i3.1310

Keywords:

Generalized Extreme Value (GEV) distribution, Gumbel distribution, Maximum Likelihood estimate (ML), Markov Chains Monte-Carlo method (MCMC), Maximum temperature, Return level

Abstract

The annual maximum temperature was modeled using the Generalized Extreme Value (GEV) distribution to Jijel weather station. The Mann-Kendall (MK) and Kwiatkowski Phillips, Schmidt and Shin (KPSS) tests suggest a stationary model without linear trend in the location parameter. The Kurtosis and the Skewness statistics indicated that the normality assumption was rejected. The Likelihood Ratio test was used to determine the best model and the goodness-of-fit tests showed that our data is suitable with a stationary Gumbel distribution. The Maximum Likelihood estimation method and the Bayesian approach using the Monte Carlo method by Markov Chains (MCMC) were used to find the parameters of the Gumbel distribution and the return levels were obtained for different periods.

JEL Classification: C1, C13, C46, C490.

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Published

27-10-2021

How to Cite

[1]
C. . HASSEN, “A bayesian analysis of the annual maximum temperature using generalized extreme value distribution”, MAUSAM, vol. 72, no. 3, pp. 607–618, Oct. 2021.

Issue

Vol. 72 No. 3 (2021): MAUSAM

Section

Research Papers

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