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 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 Alpha Power Transformed Log-Logistic Distribution with Application to Breaking Stress Data

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Maha A. Aldahlan
Keyword(s):  
Maximum Likelihood Method ◽  
Real Life ◽  
Quantile Function ◽  
Moment Generating Function ◽  
Likelihood Method ◽  
Logistic Distribution ◽  
Model Parameters ◽  
Alpha Power ◽  
New Model ◽  
Mathematical Properties

In this paper, a new three-parameter lifetime distribution is introduced; the new model is a generalization of the log-logistic (LL) model, and it is called the alpha power transformed log-logistic (APTLL) distribution. The APTLL distribution is more flexible than some generalizations of log-logistic distribution. We derived some mathematical properties including moments, moment-generating function, quantile function, Rényi entropy, and order statistics of the new model. The model parameters are estimated using maximum likelihood method of estimation. The simulation study is performed to investigate the effectiveness of the estimates. Finally, we used one real-life dataset to show the flexibility of the APTLL distribution.

Truncated Cauchy Power Lomax Model and Its Application to Biomedical Data

2020 ◽  
Vol 12 (1) ◽  
pp. 16-24
Author(s):  
Abdullah M. Almarashi
Keyword(s):  
Maximum Likelihood Method ◽  
Quantile Function ◽  
Moment Generating Function ◽  
Likelihood Method ◽  
Model Parameters ◽  
Biomedical Data ◽  
Physical Sciences ◽  
Cancer Dataset ◽  
Fundamental Properties ◽  
New Distribution

In this study, we propose a new lifetime model, named truncated Cauchy power Lomax (TCPL) distribution. The TCPL distribution has many applications in biomedical and physical sciences, and we illustrate that its application herein. We used bladder cancer dataset related to medicine to illustrate the flexibility of the TCPL distribution. The new distribution is more flexible than some well-known models. We also calculated some fundamental properties like; moments, quantile function, moment generating function and order statistics for the TCPL model. The model parameters were estimated using maximum likelihood method for estimation. At the end of the paper, the simulation study is performed to assess the effectiveness of the estimates.

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.

scholarly journals A New Flexible Three-Parameter Model: Properties, Clayton Copula, and Modeling Real Data

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 440 ◽  
Author(s):  
Abdulhakim A. Al-babtain ◽  
I. Elbatal ◽  
Haitham M. Yousof
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Real Data ◽  
Likelihood Method ◽  
Model Parameters ◽  
Simple Type ◽  
Data Sets ◽  
New Model ◽  
Clayton Copula ◽  
Mathematical Properties

In this article, we introduced a new extension of the binomial-exponential 2 distribution. We discussed some of its structural mathematical properties. A simple type Copula-based construction is also presented to construct the bivariate- and multivariate-type distributions. We estimated the model parameters via the maximum likelihood method. Finally, we illustrated the importance of the new model by the study of two real data applications to show the flexibility and potentiality of the new model in modeling skewed and symmetric data sets.

scholarly journals The Zubair-Inverse Lomax Distribution with Applications

2020 ◽  
pp. 1-14
Author(s):  
Jamilu Yunusa Falgore
Keyword(s):  
Maximum Likelihood ◽  
Moment Generating Function ◽  
Likelihood Method ◽  
Model Parameters ◽  
Data Sets ◽  
Monte Carlo Simulation Study ◽  
New Model ◽  
Inverse Lomax Distribution ◽  
The Right ◽  
Decreasing Functions

In this article, an extension of Inverse Lomax (IL) distribution with the Zubair-G family is considered . Various statistical properties of the new model where derived, including moment generating function, R´enyi entropy, and order statistics. A Monte Carlo simulation study was presented to evaluate the performance of the maximum likelihood estimators. The new model can be skew to the right, constant, and decreasing functions depending on the parameter values.We discussed the estimation of the model parameters by maximum likelihood method. The application of the new model to the data sets indicates that the new model is better than the existing competitors as it has minimum value of statistics criteria.

scholarly journals Asymptotic properties of M-estimator for GARCH(1, 1) model parameters

2020 ◽  
pp. 69-78
Author(s):  
Uladzimir S. Tserakh
Keyword(s):  
Maximum Likelihood ◽  
Heavy Tails ◽  
Maximum Likelihood Method ◽  
Asymptotic Properties ◽  
Likelihood Method ◽  
Model Parameters ◽  
Economic Time Series ◽  
Volatility Clustering ◽  
Asymptotically Normal ◽  
M Estimator

GARCH(1,  1) model is used for analysis and forecasting of financial and economic time series. In the classical version, the maximum likelihood method is used to estimate the model parameters. However, this method is not convenient for analysis of models with residuals distribution different from normal. In this paper, we consider M-estimator for the GARCH(1,  1) model parameters, which is a generalization of the maximum likelihood method. An algorithm for constructing an M-estimator is described and its asymptotic properties are studied. A set of conditions is formulated under which the estimator is strictly consistent and has an asymptotically normal distribution. This method allows to analyze models with different residuals distributions; in particular, models with stable and tempered stable distributions that allow to take into account the features of real financial data: volatility clustering, heavy tails, asymmetry.

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 A New Generalized Weighted Weibull Distribution

2019 ◽  
pp. 161-178 ◽  
Author(s):  
Salman Abbas ◽  
Gamze Ozal ◽  
Saman Hanif Shahbaz ◽  
Muhammad Qaiser Shahbaz
Keyword(s):  
Weibull Distribution ◽  
Generating Function ◽  
Probability Generating Function ◽  
Real Data ◽  
Quantile Function ◽  
Moment Generating Function ◽  
Likelihood Method ◽  
Model Parameters ◽  
Data Sets ◽  
Proposed Model

In this article, we present a new generalization of weighted Weibull distribution using Topp Leone family of distributions. We have studied some statistical properties of the proposed distribution including quantile function, moment generating function, probability generating function, raw moments, incomplete moments, probability, weighted moments, Rayeni and q th entropy. The have obtained numerical values of the various measures to see the eect of model parameters. Distribution of of order statistics for the proposed model has also been obtained. The estimation of the model parameters has been done by using maximum likelihood method. The eectiveness of proposed model is analyzed by means of a real data sets. Finally, some concluding remarks are given.

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