# Estimation of second order parameters using probability weighted moments

ESAIM: Probability and Statistics (2012)

- Volume: 16, page 97-113
- ISSN: 1292-8100

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topWorms, Julien, and Worms, Rym. "Estimation of second order parameters using probability weighted moments." ESAIM: Probability and Statistics 16 (2012): 97-113. <http://eudml.org/doc/222473>.

@article{Worms2012,

abstract = {The P.O.T. method (Peaks Over Threshold) consists in using the generalized Pareto distribution (GPD) as an approximation for the distribution of excesses over a high threshold. In this work, we use a refinement of this approximation in order to estimate second order parameters of the model using the method of probability-weighted moments (PWM): in particular, this leads to the introduction of a new estimator for the second order parameter ρ, which will be compared to other recent estimators through some simulations. Asymptotic normality results are also proved. Our new estimator of ρ looks especially competitive when |ρ| is small. },

author = {Worms, Julien, Worms, Rym},

journal = {ESAIM: Probability and Statistics},

keywords = {Extreme values; domain of attraction; excesses; generalized Pareto distribution; probability-weighted moments; second order parameter; third order condition.; extreme values; second-order parameter; third-order condition},

language = {eng},

month = {7},

pages = {97-113},

publisher = {EDP Sciences},

title = {Estimation of second order parameters using probability weighted moments},

url = {http://eudml.org/doc/222473},

volume = {16},

year = {2012},

}

TY - JOUR

AU - Worms, Julien

AU - Worms, Rym

TI - Estimation of second order parameters using probability weighted moments

JO - ESAIM: Probability and Statistics

DA - 2012/7//

PB - EDP Sciences

VL - 16

SP - 97

EP - 113

AB - The P.O.T. method (Peaks Over Threshold) consists in using the generalized Pareto distribution (GPD) as an approximation for the distribution of excesses over a high threshold. In this work, we use a refinement of this approximation in order to estimate second order parameters of the model using the method of probability-weighted moments (PWM): in particular, this leads to the introduction of a new estimator for the second order parameter ρ, which will be compared to other recent estimators through some simulations. Asymptotic normality results are also proved. Our new estimator of ρ looks especially competitive when |ρ| is small.

LA - eng

KW - Extreme values; domain of attraction; excesses; generalized Pareto distribution; probability-weighted moments; second order parameter; third order condition.; extreme values; second-order parameter; third-order condition

UR - http://eudml.org/doc/222473

ER -

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