Fitting the generalized Pareto distribution to data using maximum goodness-of-fit estimators

Luceño, Alberto

doi:10.1016/j.csda.2005.09.011

Cited by 141 publications

(91 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…90-164;Pacáková and Sodomová, 2003, pp. 30-44) or the generalised Pareto distribution (see Luceno, 2006) is also widely applied nowadays.…”

Section: A D V a N C E D M E T H O D S O F A N A L Y S I S A N D Mmentioning

confidence: 99%

Modelling of Income Distribution of Czech Households in The Years 1996-2005

Bartošová¹,

Bína²

2009

AOP

View full text Add to dashboard Cite

“…90-164;Pacáková and Sodomová, 2003, pp. 30-44) or the generalised Pareto distribution (see Luceno, 2006) is also widely applied nowadays.…”

Section: A D V a N C E D M E T H O D S O F A N A L Y S I S A N D Mmentioning

confidence: 99%

Modelling of Income Distribution of Czech Households in The Years 1996-2005

Bartošová¹,

Bína²

2009

AOP

View full text Add to dashboard Cite

“…Further, Luceño [18] discussed modifications of the standard AD statistics. The most used statistic [11,17,21] is the Right-tail AD statistics given by…”

Section: Methods Of Anderson-darling and Right-tail Anderson-darlingmentioning

confidence: 99%

“…This class of estimators are based on minimizing any empirical distribution function (EDF) statistics with respect to the unknown parameters [18].…”

Section: Methods Of Minimum Distancesmentioning

confidence: 99%

Binomial-exponential 2 Distribution: Different Estimation Methods and Weather Applications

Bakouch

Dey

Ramos³

et al. 2017

Tend. Mat. Apl. Comput.

View full text Add to dashboard Cite

ABSTRACT. In this paper, we have considered different estimation methods of the unknown parameters of a binomial-exponential 2 distribution. First, we briefly describe different methods of estimation such as maximum likelihood, method of moments, percentile based estimation, least squares, method of maximum product of spacings, method of Cramér-von-Mises, methods of Anderson-Darling and right-tail AndersonDarling, and compare them using extensive simulations studies. Finally, the potentiality of the model is studied using three real data sets related to the total monthly rainfall during April, May and September at São Carlos, Brazil.

show abstract

“…5 The Maximum Goodness of Fit-Anderson-Darling Estimator (MGFAD): Moharram et al [52] proposed least-square type estimators, which are found by minimizing the sum of squared difference between the empirical and the model quantiles. Luceño [27] proposed an estimator with a similar approach, in which the estimates are obtained by minimizing the square differences between the empirical and the model distribution functions using various Goodness of Fit statistics. Luceño [27] included the Cramer-von Mises [30], the Anderson-Darling [30], and the right-tail weighted Anderson-Darling statistics [53].…”

Section: Gpd Parameter Estimatorsmentioning

confidence: 99%

“…Several techniques are available for the estimation of GPD parameters, including MLE [39], PWM [40], LME [41], NEWLME [42], MGFAD [27], and NWLS [43].…”

Section: Gpd Parameter Estimatorsmentioning

confidence: 99%

Assessment of the Combined Effects of Threshold Selection and Parameter Estimation of Generalized Pareto Distribution with Applications to Flood Frequency Analysis

Gharib

Davies

Goss

et al. 2017

Water

View full text Add to dashboard Cite

Abstract:Floods are costly natural disasters that are projected to increase in severity and frequency into the future. Exceedances over a high threshold and analysis of their distributions, as determined through the Peak Over Threshold (POT) method and approximated by a Generalized Pareto Distribution (GPD), respectively, are widely used for flood frequency analysis. This study investigates the combined effects of threshold selection and GPD parameter estimation on the accuracy of flood quantile estimates, and develops a new, widely-applicable framework that significantly improves the accuracy of flood quantile estimations. First, the performance of several parameter estimators (i.e., Maximum Likelihood; Probability Weighted Moments; Maximum Goodness of Fit; Likelihood Moment; Modified Likelihood Moment; and Nonlinear Weighted Least Square Error) for the GPD was compared through Monte Carlo simulation. Then, a calibrated Soil and Water Assessment Tool (SWAT) model for the province of Alberta, Canada, was used to reproduce daily streamflow series for 47 watersheds distributed across the province, and the POT was applied to each. The Goodness of Fit for the resulting flood frequency models was measured by the upper tail Anderson-Darling (AD) test and the root-mean-square error (RMSE) and demonstrated improvements for more than one-third of stations by averages of 65% (AD) and 47% (RMSE), respectively.

show abstract

Fitting the generalized Pareto distribution to data using maximum goodness-of-fit estimators

Cited by 141 publications

References 11 publications

Modelling of Income Distribution of Czech Households in The Years 1996-2005

Modelling of Income Distribution of Czech Households in The Years 1996-2005

Binomial-exponential 2 Distribution: Different Estimation Methods and Weather Applications

Assessment of the Combined Effects of Threshold Selection and Parameter Estimation of Generalized Pareto Distribution with Applications to Flood Frequency Analysis

Contact Info

Product

Resources

About