Abstract

Distribution of maximum or minimum values (extreme values) of a data set is especially used in natural phenomena including flow discharge, temperature, wind speeds, precipitation and it is also used in many other  applied sciences such as reliability studies and analysis of environmental  extreme events. So if we can explain the extremes behavior via statistical formulas, we can estimate their behavior in the future. This article is devoted to study extreme values of minimum temperature in Tabriz using minimal generalized extreme value distribution, which all minima of a data set are modeled using it. In this article, we apply four methods to estimate distribution parameters including maximum likelihood estimation, probability weighted moments, elemental percentile and quantile least squares then compare estimates by average scaled absolute error criterion. We also obtain quantiles estimates and confidence intervals and finally perform goodness of fit tests.

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