Distribution of extreme values of a data set is especially used in natural phenomena including flow discharge, 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 one can model the extremal behavior, then the manner of their future behavior can be predicted. This article is devoted to study extreme wind speeds in Zahedan city using maximal generalized extreme value distribution. 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 estimation and confidence intervals. As a part of result, return period of maximum wind speeds are computed.

In this article, we propose two non-parametric estimators for the past entropy based on length-biased data, and the strong consistency of the proposed estimators is proved. In addition, some simulations are conducted to evaluate the performance of the proposed estimators. Based on the results, we show that they have better performance in a different region of the probability distribution for length-biased random variables.