On computing the maximum likelihood estimates for the q -Pareto distribution and application to earthquakes

Abstract. We define a generalized exponential distribution (G-ED) that overlaps with a generalized Pareto distribution (GPD). Then, we introduce the q-Pareto distribution (q-PD) with two-parameter family distribution (strict positive shape and scale parameters) via transforming the G-ED. We demonstrate that the q-PD can be used to model the exceedances over a threshold, and we focus to estimate the q-PD parameters. Often, the estimation is taken the maximum likelihood estimation (MLE) because have a consistent estimator with an asymptotic normal distribution and is asymptotically effective in many cases. We present an algorithm to compute the MLE for q-PD parameters with confidence intervals for each estimator. Numerical examples are given to illustrate the results obtained. Efficient numerically methods are developed for the MLE of the q-PD parameters to maximize the log-likelihood of the q-PD.

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Publication Details

Journal
Advances in statistical climatology, meteorology and oceanography
Published
2026-08-25
DOI
https://doi.org/10.5194/ascmo-12-211-2026
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
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article

On computing the maximum likelihood estimates for the q -Pareto distribution and application to earthquakes

Mohammed Ridha Kouider, Fatah Benatia
Advances in statistical climatology, meteorology and oceanography
Statistical Distribution Estimation and Applications
article

On computing the maximum likelihood estimates for the q -Pareto distribution and application to earthquakes

Mohammed Ridha Kouider, Fatah Benatia
article en

Abstract

Abstract. We define a generalized exponential distribution (G-ED) that overlaps with a generalized Pareto distribution (GPD). Then, we introduce the q-Pareto distribution (q-PD) with two-parameter family distribution (strict positive shape and scale parameters) via transforming the G-ED. We demonstrate that the q-PD can be used to model the exceedances over a threshold, and we focus to estimate the q-PD parameters. Often, the estimation is taken the maximum likelihood estimation (MLE) because have a consistent estimator with an asymptotic normal distribution and is asymptotically effective in many cases. We present an algorithm to compute the MLE for q-PD parameters with confidence intervals for each estimator. Numerical examples are given to illustrate the results obtained. Efficient numerically methods are developed for the MLE of the q-PD parameters to maximize the log-likelihood of the q-PD.

Advances in statistical climatology, meteorology and oceanographyVol. 12(2)
University Frères Mentouri Constantine 1 (DZ), University of Biskra (DZ)
Openalex Percentile: Top 7%
Statistical Distribution Estimation and Applications
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On computing the maximum likelihood estimates for the q -Pareto distribution and application to earthquakes — Mohammed Ridha Kouider, Fatah Benatia · Advances in statistical climatology, meteorology and oceanography (2026) | TGRS Research Map | TGRS