On the characterization of generalized extreme value, power function, generalized Pareto and classical Pareto distributions by conditional expectation of record values

2001 ◽  
Vol 42 (2) ◽  
pp. 225-242 ◽  
Author(s):  
Jong-Wuu Wu ◽  
Wen-Chuan Lee
Keyword(s):  
Power Function ◽  
Conditional Expectation ◽  
Record Values ◽  
Extreme Value ◽  
Generalized Extreme Value ◽  
Generalized Pareto ◽  
Pareto Distributions

Inference on P(Y

2020 ◽  
Vol 72 (2) ◽  
pp. 89-110
Author(s):  
Manoj Chacko ◽  
Shiny Mathew
Keyword(s):  
Maximum Likelihood ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Maximum Likelihood Estimators ◽  
Record Values ◽  
Asymptotic Distributions ◽  
Bayes Estimators ◽  
Generalized Pareto ◽  
Pareto Distributions ◽  
Generalized Pareto Distributions

In this article, the estimation of [Formula: see text] is considered when [Formula: see text] and [Formula: see text] are two independent generalized Pareto distributions. The maximum likelihood estimators and Bayes estimators of [Formula: see text] are obtained based on record values. The Asymptotic distributions are also obtained together with the corresponding confidence interval of [Formula: see text]. AMS 2000 subject classification: 90B25

scholarly journals Hujan rancangan berdasarkan analisis frekuensi regional dengan metode tl-moment

2017 ◽  
Vol 12 (1) ◽  
pp. 1-16
Author(s):  
Segel Ginting ◽  
William M Putuhena
Keyword(s):  
Frequency Analysis ◽  
Probability Distributions ◽  
Large Error ◽  
Extreme Value ◽  
Regional Frequency Analysis ◽  
Generalized Extreme Value ◽  
Generalized Pareto ◽  
Moment Ratio ◽  
Moments Method ◽  
Regional Frequency

The designstorm wereestimated by applying the regional frequency analysis provides benefits to a datasetwith limited amount of data has many advantages. Minimum data used in calculating the amount of design stromhas a very large error for higherreturn period. Therefore, the regional frequency analysis was used based on TL-moments method. There arethree types of probability distributions used in this study, namely the Generalized Extreme Value (GEV), Generalized Pareto (GPA) and the Generalized Logistic (GLO). Two of the three typesprobability distributions are the best choice by the TL-moment ratio diagrams which are Generalized Extreme Value, and Generalized Logistic. Ananother analysis wasconducted by the Z test and the Generalized Extreme Value (GEV) gives the best results. Therefore, the designs strom which was estimated based on the regional frequency analysis in Jakarta watersheds using the Generalized Extreme Value (GEV) has been determined.

scholarly journals Regional analysis of maximum rainfall using L-moment and LH-moment: A comparative case study for the northeast India

MAUSAM ◽  
2021 ◽  
Vol 68 (3) ◽  
pp. 451-462
Author(s):  
DHRUBA JYOTI BORA ◽  
MUNINDRA BORAH ◽  
ABHIJIT BHUYAN
Keyword(s):  
Frequency Analysis ◽  
Moment Method ◽  
Probability Distributions ◽  
Northeast India ◽  
Extreme Value ◽  
Generalized Extreme Value ◽  
Generalized Pareto ◽  
Rainfall Frequency ◽  
Best Fitting ◽  
Rainfall Frequency Analysis

Rainfall data of the northeast region of India has been considered for selecting best fit model for rainfall frequency analysis. The methods of L-moment has been employed for estimation of parameters five probability distributions, namely Generalized extreme value (GEV), Generalized Logistic(GLO), Pearson type 3 (PE3), 3 parameter Log normal (LN3) and Generalized Pareto (GPA) distributions. The methods of LH-moment of four orders (L1 L2, L3 & L4-moments) have also been used for estimating the parameters of three probability distributions namely Generalized extreme value (GEV), Generalized Logistic (GLO) and Generalized Pareto (GPA) distributions. PE3 distribution has been selected as the best fitting distribution using L-moment, GPA distribution using L1-moment and GLO distribution using L2, L3 & L4-moments. Relative root mean square error (RRMSE) and RBIAS are employed to compare between the results found from L-moment and LH-moment analysis. It is found that GPA distribution designated by L1-moment method is the most suitable and the best fitting distribution for rainfall frequency analysis of the northeast India. Also the L1-moment method is significantly more efficient than L-moment and other orders of LH-moment for rainfall frequency analysis of the northeast India.

scholarly journals Sebaran Generalized Extreme Value (GEV) Dan Generalized Pareto (GP) untuk Pendugaan Curah Hujan Ekstrim di Wilayah DKI Jakarta

2016 ◽  
Vol 7 (1) ◽  
pp. 75-84
Author(s):  
Achi Rinaldi
Keyword(s):  
Pareto Distribution ◽  
Extreme Event ◽  
Generalized Pareto Distribution ◽  
Extreme Value Distribution ◽  
Location Parameter ◽  
Extreme Rainfall ◽  
Extreme Value ◽  
Generalized Extreme Value Distribution ◽  
Generalized Extreme Value ◽  
Generalized Pareto

Extreme event such as extreme rainfall have been analyzed and most concern for the country all around the world. There are two common distribution for extreme value which are Generalized Extreme Value distribution and Generalized Pareto distribution. These two distribution have shown good performace to estimate the parameter of  extreme value. This research was aim to estimate parameter of extreme value using GEV distribution and GP distribution, and also to characterized effect of extreme event such as flood. The rainfall data was taken from BMKG for 5 location in DKI Jakarta. Both of distribution shown a good perfromance. The resut showed that Tanjung Priok station has biggest location parameter for GEV and also the biggest scale parameter for GP, that mean the biggest probability to take flood effect of the extreme rainfall.

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