The McDonald Gumbel model

2016 ◽  
Vol 45 (11) ◽  
pp. 3367-3382 ◽  
Author(s):  
Edleide de Brito ◽  
Giovana Oliveira Silva ◽  
Gauss M. Cordeiro ◽  
Clarice Garcia B. Demétrio
Keyword(s):  
Gumbel Model

scholarly journals Simple method for estimating daily and  total COVID-19 deaths using a Gumbel model

2020 ◽  
Author(s):  
Hiroshi Furutani ◽  
Tomoyuki Hiroyasu ◽  
Yoshiyasu Okuhara
Keyword(s):  
New York ◽  
New York City ◽  
Distribution Function ◽  
United Kingdom ◽  
York City ◽  
Gumbel Distribution ◽  
Series Data ◽  
Simple Method ◽  
The United Kingdom ◽  
Gumbel Model

Abstract The purpose of the present paper is to introduce a method for forecasting daily and total numbers of COVID-19-associated deaths. We apply the Gumbel distribution function for the analysis of time series data of the first wave. The Gumbel distribution function F(t) has a notable property of F(t) = 1/2.718 at the node (peak) point of the distribution. Therefore, we can forecast the number of total deaths N. In the present study, the Gumbel model gives the estimate N ≈ 2.718N1, where N1 is the partial sum of the daily numbers of deaths until the day of the peak. The proposed model can also forecast the daily numbers after the peak. We investigated the data of New York City, Belgium, Switzerland, Sweden, and the United Kingdom. The Gumbel model gives reasonable results for New York City, Belgium, and Switzerland. On the other hand, the proposed method underestimates N for Sweden and the United Kingdom. The proposed approach is very simple, and carrying out the analysis is easy. This method uses spreadsheet software for most of the calculations, and no special program is needed.

Statistical Uncertainties in Wave Heights and Combined Loads on Offshore Structures

1991 ◽  
Vol 113 (2) ◽  
pp. 156-161
Author(s):  
S. R. Winterstein ◽  
S. Haver
Keyword(s):  
Environmental Variables ◽  
Probabilistic Models ◽  
Offshore Structures ◽  
Annual Maximum ◽  
Storm Events ◽  
Base Shear ◽  
Combined Loads ◽  
Wave Heights ◽  
Gumbel Model ◽  
Non Gaussian

Probabilistic models of combined environmental variables are shown, and their effect on the probability distribution of annual maximum base shear is estimated. A new “generalized Gumbel” model is introduced for the critical wave height parameter. By preserving higher statistical moments, this model better follows extreme storm events. Uncertainty in this model is included through statistical uncertainty in these moments. Corresponding reliability confidence intervals are also shown as a function of the sample size of hindcast data. Finally, models of the non-Gaussian crest and the drag parameter are found to be of similar importance in predicting the 100-yr base shear.

Bayesian Estimation, Prediction and Characterization for the Gumbel Model Based on Records

Statistics ◽  
2002 ◽  
Vol 36 (1) ◽  
pp. 65-74 ◽  
Author(s):  
M.A.M. Ali Mousa ◽  
Z.F. Jaheen ◽  
A.A. Ahmad
Keyword(s):  
Bayesian Estimation ◽  
Model Based ◽  
Gumbel Model

Efficient Fitting Procedure for Corrosion Rate Distribution in View of Reliability Analysis

2017 ◽  
Author(s):  
Shinsuke Sakai ◽  
Takuyo Kaida
Keyword(s):  
Monte Carlo Simulation ◽  
Corrosion Rate ◽  
Reliability Analysis ◽  
Estimation Of Parameters ◽  
Tail Region ◽  
Rate Distribution ◽  
Fitting Procedure ◽  
Fitting Procedures ◽  
Gumbel Model ◽  
Fitting Accuracy

The Gumbel model is widely used for the theoretical distribution of the corrosion rate. In applying the reliability analysis, the parameters of the distribution must be estimated from the inspected data. The estimation of parameters is done by using some fitting procedures. However, it is not necessarily clear which fitting procedure is suitable in view of reliability analysis. Especially, the fitting accuracy around tail region is possibly influence the reliability analysis. In this study, the efficient fitting procedure for the corrosion rate distribution in view of reliability analysis was investigated using Monte Carlo Simulation together with reliability analysis.

scholarly journals Effect of warming climate on extreme daily rainfall depth using non-stationary Gumbel model with temperature co-variate

2021 ◽  
Author(s):  
Okjeong Lee ◽  
Inkyeong Sim ◽  
Sangdan Kim
Keyword(s):  
Scale Parameter ◽  
Surface Air Temperature ◽  
Daily Rainfall ◽  
Gumbel Distribution ◽  
Extreme Rainfall ◽  
Dew Point ◽  
Warming Climate ◽  
Rainfall Depth ◽  
Gumbel Model ◽  
Two Parameters

Abstract In this study, non-stationary frequency analysis was carried out to apply non-stationarity of extreme rainfall driven by climate change using the scale parameter of two parameters of the Gumbel distribution (GUM) as a co-variate function. The surface air temperature (SAT) or dew-point temperature (DPT) is applied as the co-variate. The optimal model was selected by comparing AICs, and 17 of 60 sites were found to be suitable for the non-stationary GUM model. In addition, SAT was chosen as the more appropriate co-variate among 13 of the 17 sites. As a result of estimating changes in design rainfall depth with future SAT rises at 13 sites, it is likely to increase by 10% in 2040 and 18% in 2070.

MODELS BASED ON PARTIAL INFORMATION ABOUT SURVIVAL AND HAZARD GRADIENT

2010 ◽  
Vol 24 (4) ◽  
pp. 561-584 ◽  
Author(s):  
Majid Asadi ◽  
Somayeh Ashrafi ◽  
Nader Ebrahimi ◽  
Ehsan S. Soofi
Keyword(s):  
Joint Distribution ◽  
Partial Information ◽  
Survival Function ◽  
Information Criteria ◽  
Hazard Rates ◽  
Optimal Model ◽  
Dynamic Information ◽  
Hazard Gradient ◽  
Other Information ◽  
Gumbel Model

This article develops information optimal models for the joint distribution based on partial information about the survival function or hazard gradient in terms of inequalities. In the class of all distributions that satisfy the partial information, the optimal model is characterized by well-known information criteria. General results relate these information criteria with the upper orthant and the hazard gradient orderings. Applications include information characterizations of the bivariate Farlie–Gumbel–Morgenstern, bivariate Gumbel, and bivariate generalized Gumbel, for which no other information characterization are available. The generalized bivariate Gumbel model is obtained from partial information about the survival function and hazard gradient in terms of marginal hazard rates. Other examples include dynamic information characterizations of the bivariate Lomax and generalized bivariate Gumbel models having marginals that are transformations of exponential such as Pareto, Weibull, and extreme value. Mixtures of bivariate Gumbel and generalized Gumbel are obtained from partial information given in terms of mixtures of the marginal hazard rates.

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