• Journal of the Chungcheong Mathematical Society
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• v.27 no.3
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• pp.347-361
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• 2014

Generalized Pareto distribution play an important role in reliability, extreme value theory, and other branches of applied probability and statistics. This family of distribution includes exponential distribution, Pareto or Lomax distribution. In this paper, we established exact expressions and recurrence relations satised by the quotient moments of generalized order statistics for a generalized Pareto distribution. Further the results for quotient moments of order statistics and records are deduced from the relations obtained and a theorem for characterizing this distribution is presented.

• Journal of applied mathematics & informatics
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• v.31 no.3_4
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• pp.327-336
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• 2013

Generalized Pareto distributions play an important role in re-liability, extreme value theory, and other branches of applied probability and statistics. This family of distribution includes exponential distribution, Pareto distribution, and Power distribution. In this paper we establish some recurrences relations satisfied by the quotient moments of the upper record values from the generalized Pareto distribution. Further a char-acterization of this distribution based on recurrence relations of quotient moments of record values is presented.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1521-1529
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• 2013

In this paper, we develop noninformative priors for the generalized Pareto distribution when the scale parameter is of interest. We developed the rst order and the second order matching priors. We revealed that the second order matching prior does not exist. It turns out that the reference prior and Jeffrey's prior do not satisfy a first order matching criterion, and Jeffreys' prior, the reference prior and the matching prior are different. Some simulation study is performed and a real example is given.

• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.793-798
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• 2008

Extreme value inference deals with fitting the generalized extreme value distribution model and the generalized Pareto distribution model, which are recently combined to give a single model, namely a two-dimensional non-homogeneous Poisson exceedance point process model. In this paper, we extend the two-dimensional non-homogeneous Poisson process model to include non-stationary effect or dependence on covariates and then derive the likelihood for the extended model.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.967-972
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• 2010

We shall consider estimations of an exponetiated parameter of the exponentiated Pareto distribution with known scale and threshold parameters. A quotient distribution of two independent exponentiated Pareto random variables is obtained. We also derive the distribution of the ratio of two independent exponentiated Pareto random variables.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.799-803
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• 2003

We obtained the best linear unbiased estimator for the scale parameter of the generalized Pareto Distribution by the order statistics.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.463-477
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• 2009

Parameter estimation methods such as maximum likelihood estimation method, probability weighted moments method, regression method have been popularly applied to various extreme value models in numerous literature. Among three methods above, the performance of regression method has not been rigorously investigated yet. In this paper the regression method is compared with the other methods via Monte Carlo simulation studies for estimation of parameters of the Generalized Extreme Value(GEV) distribution and the Generalized Pareto(GP) distribution. Our simulation results indicate that the regression method tends to outperform other methods under small samples by providing smaller biases and root mean square errors for estimation of location parameter of the GEV model. For the scale parameter estimation of the GP model under small samples, the regression method tends to report smaller biases than the other methods. The regression method tends to be superior to other methods for the shape parameter estimation of the GEV model and GP model when the shape parameter is -0.4 under small and moderately large samples.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.947-958
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• 2014

Understanding extreme precipitation events is very important for flood planning purposes. Especially, the r-year return level is a common measure of extreme events. In this paper, we present a spatial analysis of precipitation return level using hierarchical Bayesian modeling. For intensity, we model annual maximum daily precipitations and daily precipitation above a high threshold at 62 stations in Korea with generalized extreme value(GEV) and generalized Pareto distribution(GPD), respectively. The spatial dependence among return levels is incorporated to the model through a latent Gaussian process of the GEV and GPD model parameters. We apply the proposed model to precipitation data collected at 62 stations in Korea from 1973 to 2011.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.931-939
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• 2011

In practice, thresholds are determined by the two subjective assessment methods in a generalized pareto distribution of mean extreme function(MEF-graph) or Hill-graph. To remedy the problem of subjectiveness of these methods, we propose an alternative method to determine the threshold based on the robust statistics. We compared the MEF-graph, Hill-graph and our method through VaRs on the Korean stock market data from January 5, 1987 to August 3, 2009. As a result, the VaR based on the proposed method is not much different from the existing methods, and the standard deviation of VaR for our method was the smallest. The results show that our method can be a promising alternative to determine thresholds of the generalized pareto distributions.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.171-178
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• 2013

In this paper, we develop noninformative priors for the generalized Pareto distribution when the parameter of interest is the shape parameter. We developed the first order and the second order matching priors.We revealed that the second order matching prior does not exist. It turns out that the reference prior satisfies a first order matching criterion, but Jeffrey's prior is not a first order matching prior. Some simulation study is performed and a real example is given.