scholarly journals Estimation of P(Y < X) in a Four-Parameter Generalized Gamma Distribution

2016 ◽  
Vol 41 (3) ◽  
Author(s):  
M. Masoom Ali ◽  
Manisha Pal ◽  
Jungsoo Woo
Keyword(s):  
Maximum Likelihood ◽  
Closed Form ◽  
Importance Sampling ◽  
Simulation Study ◽  
Maximum Likelihood Method ◽  
Sampling Procedure ◽  
Likelihood Method ◽  
Scale Parameters ◽  
Generalized Gamma ◽  
Modified Maximum Likelihood

In this paper we consider estimation of R = P(Y < X), when X and Y are distributed as two independent four-parameter generalized gamma random variables with same location and scale parameters. A modified maximum likelihood method and a Bayesian technique have been used to estimate R on the basis of independent samples. As the Bayes estimator cannot be obtained in a closed form, it has been implemented using importance sampling procedure. A simulation study has also been carried out to compare the two methods.

scholarly journals Data bank: nine numerical methods for determining the parameters of weibull for wind energy generation tested by five statistical tools

2021 ◽  
Vol 12 (2) ◽  
pp. 1114
Author(s):  
Ahmed Samir Badawi ◽  
Siti Hajar Yusoff ◽  
Alhareth Mohammed Zyoud ◽  
Sheroz Khan ◽  
Aisha Hashim ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Numerical Methods ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Prediction Accuracy ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Mean Square ◽  
Modified Maximum Likelihood

This study aims to determine the potential of wind energy in the mediterranean coastal plain of Palestine. The parameters of the Weibull distribution were calculated on basis of wind speed data. Accordingly, two approaches were employed: analysis of a set of actual time series data and theoretical Weibull probability function. In this analysis, the parameters Weibull shape factor ‘<em>k</em>’ and the Weibull scale factor ‘<em>c</em>’ were adopted. These suitability values were calculated using the following popular methods: method of moments (MM), standard deviation method (STDM), empirical method (EM), maximum likelihood method (MLM), modified maximum likelihood method (MMLM), second modified maximum likelihood method (SMMLM), graphical method (GM), least mean square method (LSM) and energy pattern factor method (EPF). The performance of these numerical methods was tested by root mean square error (RMSE), index of agreement (IA), Chi-square test (X<sup>2</sup>), mean absolute percentage error (MAPE) and relative root mean square error (RRMSE) to estimate the percentage of error. Among the prediction techniques. The EPF exhibited the greatest accuracy performance followed by MM and MLM, whereas the SMMLM exhibited the worst performance. The RMSE achieved the best prediction accuracy, whereas the RRMSE attained the worst prediction accuracy.

scholarly journals The Modified Beta Gompertz Distribution: Theory and Applications

Mathematics ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 3
Author(s):  
Ibrahim Elbatal ◽  
Farrukh Jamal ◽  
Christophe Chesneau ◽  
Mohammed Elgarhy ◽  
Sharifah Alrajhi
Keyword(s):  
Maximum Likelihood ◽  
Probability Distribution ◽  
Simulation Study ◽  
Maximum Likelihood Method ◽  
Maximum Likelihood Estimators ◽  
Quantile Function ◽  
Moment Generating Function ◽  
Likelihood Method ◽  
Model Parameters ◽  
Gompertz Distribution

In this paper, we introduce a new continuous probability distribution with five parameters called the modified beta Gompertz distribution. It is derived from the modified beta generator proposed by Nadarajah, Teimouri and Shih (2014) and the Gompertz distribution. By investigating its mathematical and practical aspects, we prove that it is quite flexible and can be used effectively in modeling a wide variety of real phenomena. Among others, we provide useful expansions of crucial functions, quantile function, moments, incomplete moments, moment generating function, entropies and order statistics. We explore the estimation of the model parameters by the obtained maximum likelihood method. We also present a simulation study testing the validity of maximum likelihood estimators. Finally, we illustrate the flexibility of the distribution by the consideration of two real datasets.

scholarly journals Quartile Method Estimation of Two-Parameter Exponential Distribution Data with Outliers

2016 ◽  
Vol 5 (5) ◽  
pp. 12
Author(s):  
Entisar A. Elgmati ◽  
Nadia B. Gregni
Keyword(s):  
Maximum Likelihood ◽  
Exponential Distribution ◽  
Simulation Study ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Distribution Data ◽  
Unknown Parameters ◽  
First Order ◽  
Minimum Statistics ◽  
Two Parameter

Several methods have been used to estimate the unknown parameters in the two-parameter exponential distribution. Here we have considered two of these methods, maximum likelihood method and median-first order statistics method. However, in the presence of outliers these methods are not valid. In this paper we propose two approaches that deal with this situation. The idea is based on using first and third quartile instead of the minimum statistics. We investigated the parameters estimate using these methods through simulation study. The new method gives similar results under the normal situation and much better results when the data has outliers.

scholarly journals Estimation of parameters of Nadarajah-Haghighi extension of the exponential distribution using perfect and imperfect ranked set sample

2020 ◽  
Vol 30 (2) ◽  
pp. 177-198
Author(s):  
Marija Minic
Keyword(s):  
Maximum Likelihood ◽  
Exponential Distribution ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Simple Random Sample ◽  
Estimation Of Parameters ◽  
Ranked Set Sample ◽  
Scale Parameters ◽  
Wide Range ◽  
Mean Square Errors

The ranked set sampling (RSS) is a cost-effective method of sampling that can be used in a wide range of statistical problems. In this paper, the shape and the scale parameters of Nadarajah-Haghighi extension of the exponential distribution are estimated based on a simple random sample (SRS) and RSS. Three cases are considered: 1) the scale parameter is known; 2) the shape parameter is known; 3) both shape and scale parameters are unknown. Observations are done when the ranking mechanism in the ranked set sample is perfect and when it is not. Method of moments, the maximum likelihood method, and a modification of the maximum likelihood method are used. The obtained estimators are compared in terms of their biases and mean square errors (MSE). The results revealed that estimators based on RSS tend to show better properties (smaller bias and MSE) relative to their SRS counterparts, regardless of the quality of the ranking.

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