Modeling extreme events: Sample fraction adaptive choice in parameter estimation

Modeling Extreme Events: Sample Fraction Adaptive Choice in Parameter Estimation

Journal of Statistical Theory and Practice ◽

10.1080/15598608.2014.890984 ◽

2014 ◽

Vol 9 (1) ◽

pp. 184-199 ◽

Cited By ~ 10

Author(s):

M. Manuela Neves ◽

M. Ivette Gomes ◽

Fernanda Figueiredo ◽

Dora Prata Gomes

Keyword(s):

Parameter Estimation ◽

Extreme Events ◽

Adaptive Choice ◽

Sample Fraction

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Frequency Analysis of Nonidentically Distributed Hydrometeorological Extremes Associated with Large-Scale Climate Variability Applied to South Korea

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-13-0200.1 ◽

2014 ◽

Vol 53 (5) ◽

pp. 1193-1212 ◽

Cited By ~ 8

Author(s):

Taesam Lee ◽

Changsam Jeong

Keyword(s):

Parameter Estimation ◽

Climate Variability ◽

South Korea ◽

Simulation Study ◽

Extreme Events ◽

Large Scale ◽

Southern Oscillation ◽

Estimation Method ◽

Parameter Estimation Method

AbstractIn the frequency analyses of extreme hydrometeorological events, the restriction of statistical independence and identical distribution (iid) from year to year ensures that all observations are from the same population. In recent decades, the iid assumption for extreme events has been shown to be invalid in many cases because long-term climate variability resulting from phenomena such as the Pacific decadal variability and El Niño–Southern Oscillation may induce varying meteorological systems such as persistent wet years and dry years. Therefore, the objective of the current study is to propose a new parameter estimation method for probability distribution models to more accurately predict the magnitude of future extreme events when the iid assumption of probability distributions for large-scale climate variability is not adequate. The proposed parameter estimation is based on a metaheuristic approach and is derived from the objective function of the rth power probability-weighted sum of observations in increasing order. The combination of two distributions, gamma and generalized extreme value (GEV), was fitted to the GEV distribution in a simulation study. In addition, a case study examining the annual hourly maximum precipitation of all stations in South Korea was performed to evaluate the performance of the proposed approach. The results of the simulation study and case study indicate that the proposed metaheuristic parameter estimation method is an effective alternative for accurately selecting the rth power when the iid assumption of extreme hydrometeorological events is not valid for large-scale climate variability. The maximum likelihood estimate is more accurate with a low mixing probability, and the probability-weighted moment method is a moderately effective option.

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A New Technique for the Analysis of Extreme Rainfall with Application to Lagos Metropolis, Nigeria

Hydrology Research ◽

10.2166/nh.1983.0011 ◽

1983 ◽

Vol 14 (3) ◽

pp. 127-138 ◽

Cited By ~ 1

Author(s):

E. S. Oyegoke ◽

J. O. Sonuga

Keyword(s):

Parameter Estimation ◽

Maximum Entropy ◽

Extreme Events ◽

Gumbel Distribution ◽

Extreme Rainfall ◽

Statistical Simulation ◽

Entropy Method ◽

Likelihood Method ◽

Rainfall Analysis ◽

Storm Rainfall

This paper focuses on the use of the principle of maximum entropy as an alternative technique for the parameter estimation of the Extreme Value Type – 1 (EV1) distribution or Gumbel distribution often used for the analysis and forecast of extreme events. A case study is made of storm rainfall analysis for Lagos metropolis using the available rainfall data for Ikeja, Oshodi and Lagos Mainland as obtained from Akanbi (1982). For comparison purposes, the parameters of the EV1 distribution is also obtained using the Maximum Likelihood Method. The later being one of the most reliable techniques and perhaps the most widely used for parameter estimation of the EV1 distribution. This exercise has made it possible to demonstrate in some ways the superiority of the maximum entropy method over existing methods used for statistical simulation of extreme events.

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