scholarly journals A Bivariate Gamma Distribution Whose Marginals are Finite Mixtures of Gamma Distributions

2020 ◽  
Vol 8 (4) ◽  
pp. 950-971
Author(s):  
Maryam Rafiei ◽  
Anis Iranmanesh ◽  
Daya k. Nagar
Keyword(s):  
Maximum Likelihood ◽  
Gamma Distribution ◽  
Maximum Likelihood Method ◽  
Correlation Coefficients ◽  
Moment Generating Function ◽  
Bivariate Distribution ◽  
Finite Mixture ◽  
Likelihood Method ◽  
Gamma Distributions ◽  
Bivariate Gamma Distribution

In this article a new bivariate distribution, whose both the marginals are finite mixture of gamma distribution has been defined. Several of its properties such moments, correlation coefficients, measure of skewness, moment generating function, Renyi and Shannon entropies have been derived. Simulation study have been conducted to evaluate the performance of maximum likelihood method.

scholarly journals The Weibull-Gamma Distribution: Properties and Applications

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 438 ◽  
Author(s):  
Hadeel S. Klakattawi
Keyword(s):  
Maximum Likelihood ◽  
Gamma Distribution ◽  
Maximum Likelihood Method ◽  
Data Distribution ◽  
Likelihood Method ◽  
Parameter Distribution ◽  
Great Flexibility ◽  
Special Cases ◽  
New Distribution ◽  
Family Of Distributions

A new member of the Weibull-generated (Weibull-G) family of distributions—namely the Weibull-gamma distribution—is proposed. This four-parameter distribution can provide great flexibility in modeling different data distribution shapes. Some special cases of the Weibull-gamma distribution are considered. Several properties of the new distribution are studied. The maximum likelihood method is applied to obtain an estimation of the parameters of the Weibull-gamma distribution. The usefulness of the proposed distribution is examined by means of five applications to real datasets.

scholarly journals THE TRANSMUTED GAMMA-GOMPERTZ DISTRIBUTION

2020 ◽  
Vol 8 (10) ◽  
pp. 236-248
Author(s):  
Rwabi AzZwideen ◽  
Loai M. Al Zou’bi
Keyword(s):  
Maximum Likelihood ◽  
Order Statistics ◽  
Generating Function ◽  
Maximum Likelihood Method ◽  
Probability Model ◽  
Quantile Function ◽  
Moment Generating Function ◽  
Likelihood Method ◽  
Gompertz Distribution ◽  
Proposed Model

This article introduces a four-parameter probability model which represents a gener- alization of the the Gamma-Gompertz distribution using the quadratic rank trans- mutation map. The proposed model is named the Transmuted Gamma-Gompertz distribution. We provide explicit expressions for its statistical properties, moment generating function, quantile function, the order statistics, the quantile function and the median. We estimate the parameters of the distribution using the maximum likelihood method of estimation.

scholarly journals Inverse Lomax-Exponentiated G (IL-EG) Family of Distributions: Properties and Applications

2020 ◽  
pp. 48-64
Author(s):  
Jamilu Yunusa Falgore ◽  
Sani Ibrahim Doguwa
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Moment Generating Function ◽  
Maximum Likelihood Estimates ◽  
Likelihood Method ◽  
Data Sets ◽  
Baseline Model ◽  
New Family ◽  
Family Of Distributions ◽  
Better Than

A new generator of continuous distributions called the Inverse Lomax-Exponentiated G family, which has three extra positive parameters is proposed. The structural properties of the new family that holds for any continuous baseline model including explicit density function expressions, moments, inequality measurements, moment generating function, reliability functions, Renyi and Shanon entropies, and distribution of order statistics are derived. A Monte Carlo simulation to test the efficiency of the maximum likelihood estimates is conducted. The application of the new sub-model to the two data sets using the maximum likelihood method indicates that the new model is better than the existing competitors.

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 Lomax Weibull Distribution

2019 ◽  
pp. 1-18 ◽  
Author(s):  
Benjamin Apam ◽  
Nasiru Suleman ◽  
Emmanuel Adjei
Keyword(s):  
Distribution Function ◽  
Maximum Likelihood ◽  
Probability Distribution ◽  
Probability Distribution Function ◽  
Maximum Likelihood Method ◽  
Moment Generating Function ◽  
Likelihood Method ◽  
Survival Functions ◽  
Score Functions ◽  
The Moment

In this article, we introduce the Lomax-Weibull (LoW) distribution using the method of composition of CDFs from the Lomax and Weibull distributions. Expressions for the moment generating function, hazard and survival functions were derived. A plot of the probability distribution function and cumulative distributions were done using the Python software. We also used the maximum likelihood method of estimation to derive the score functions for estimating the parameters of the distribution.

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