scholarly journals Asymmetric Bimodal Exponential Power Distribution on Real Line

2017 ◽  
Author(s):  
Mehmet Niyazi Çankaya
Keyword(s):  
Real Line ◽  
Power Distribution ◽  
Information Matrix ◽  
Real Data ◽  
Maximum Likelihood Estimates ◽  
Data Generation ◽  
Artificial Data ◽  
Exponential Power Distribution ◽  
Exponential Power ◽  
Two Parameters

The asymmetric bimodal exponential power (ABEP) distribution is an extension of the generalized gamma distribution to the real line via adding two parameters which fit the shape of peakedness in bimodality on real line. The special values of peakedness parameters of the distribution are combination of half Laplace and half normal distributions on real line. The distribution has two parameters fitting the height of bimodality, so capacity of bimodality is enhanced by using these parameters. Adding a skewness parameter is considered to model asymmetry in data. The location-scale form of this distribution is proposed. The Fisher information matrix of these parameters in ABEP is obtained explicitly. Properties of ABEP are examined. Real data examples are given to illustrate the modelling capacity of ABEP. The replicated artificial data from maximum likelihood estimates of parameters of ABEP and distributions having an algorithm for artificial data generation procedure are provided to test the similarity with real data.

scholarly journals The Censored Beta-Skew Alpha-Power Distribution

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1114
Author(s):  
Guillermo Martínez-Flórez ◽  
Roger Tovar-Falón ◽  
María Martínez-Guerra
Keyword(s):  
Power Distribution ◽  
Information Matrix ◽  
Real Data ◽  
Likelihood Method ◽  
Data Sets ◽  
Alpha Power ◽  
Score Functions ◽  
New Family ◽  
Two Parameters ◽  
Family Of Distributions

This paper introduces a new family of distributions for modelling censored multimodal data. The model extends the widely known tobit model by introducing two parameters that control the shape and the asymmetry of the distribution. Basic properties of this new family of distributions are studied in detail and a model for censored positive data is also studied. The problem of estimating parameters is addressed by considering the maximum likelihood method. The score functions and the elements of the observed information matrix are given. Finally, three applications to real data sets are reported to illustrate the developed methodology.

scholarly journals The Location-Scale Mixture Exponential Power Distribution: A Bayesian and Maximum Likelihood Approach

2012 ◽  
Vol 2012 ◽  
pp. 1-14
Author(s):  
Z. Rahnamaei ◽  
N. Nematollahi ◽  
R. Farnoosh
Keyword(s):  
Maximum Likelihood ◽  
Power Distribution ◽  
Real Data ◽  
Scale Mixture ◽  
Exponential Power Distribution ◽  
Likelihood Approach ◽  
Slash Distribution ◽  
Exponential Power ◽  
Mean Square Errors ◽  
New Distribution

We introduce an alternative skew-slash distribution by using the scale mixture of the exponential power distribution. We derive the properties of this distribution and estimate its parameter by Maximum Likelihood and Bayesian methods. By a simulation study we compute the mentioned estimators and their mean square errors, and we provide an example on real data to demonstrate the modeling strength of the new distribution.

scholarly journals Poisson Exponential Power Distribution: Properties and Application

2020 ◽  
Vol 08 (11) ◽  
Author(s):  
Vijay Kumar ◽  
Keyword(s):  
Power Distribution ◽  
Statistical Tests ◽  
Information Matrix ◽  
Likelihood Estimation ◽  
Reliability Function ◽  
Rate Function ◽  
Cumulative Distribution ◽  
Lifetime Distributions ◽  
Exponential Power Distribution ◽  
Exponential Power

In this study, we have established a new three-parameter Poisson Exponential Power distribution using the Poisson-G family of distribution. We have presented the mathematical and statistical properties of the proposed distribution including probability density function, cumulative distribution function, reliability function, hazard rate function, quantile, the measure of skewness, and kurtosis. The parameters of the new distribution are estimated using the maximum likelihood estimation (MLE) method, and constructed the asymptotic confidence intervals also the Fisher information matrix is derived analytically to obtain the variance-covariance matrix for MLEs. All the computations are performed in R software. The potentiality of the proposed distribution is revealed by using some graphical methods and statistical tests taking a real dataset. We have empirically proven that the proposed distribution provided a better fit and more flexible in comparison with some other lifetime distributions.

scholarly journals Generalized Exponential Power Distribution with Application to Complete and Censored Data

2021 ◽  
pp. 34-55
Author(s):  
M. M. E. Abd El-Monsef ◽  
M. M. El-Awady
Keyword(s):  
Maximum Likelihood ◽  
Censored Data ◽  
Power Distribution ◽  
Real Data ◽  
Likelihood Method ◽  
Model Parameters ◽  
Type I ◽  
Sample Type ◽  
Exponential Power Distribution ◽  
Exponential Power

The exponential power distribution (EP) is a lifetime model that can exhibit increasing and bathtub hazard rate function. This paper proposed a generalization of EP distribution, named generalized exponential power (GEP) distribution. Some properties of GEP distribution will be investigated. Recurrence relations for single moments of generalized ordered statistics from GEP distribution are established and used for characterizing the GEP distribution. Estimation of the model parameters are derived using maximum likelihood method based on complete sample, type I, type II and random censored samples. A simulation study is performed in order to examine the accuracy of the maximum likelihood estimators of the model parameters. Three applications to real data, two with censored data, are provided in order to show the superiority of the proposed model to other models.

scholarly journals Bayesian Estimation and Prediction of Three-Parameter Complementary Exponential Power Distribution using MCMC Technique

2020 ◽  
Vol 10 (2) ◽  
pp. 164-174
Keyword(s):  
Power Distribution ◽  
Real Data ◽  
Credible Interval ◽  
Simulation Method ◽  
Complete Sample ◽  
Unknown Parameters ◽  
Data Set ◽  
Exponential Power Distribution ◽  
Exponential Power ◽  
Bayesian Estimators

The Markov chain Monte Carlo (MCMC) technique is applied for estimating the Complementary Exponential Power (CEP) distribution's parameters through the analysis of complete sample in this article. With the help of the Bayesian and the Maximum Likelihood techniques, the unknown parameters of the model are estimated. To find Complementary Exponential Power distribution's parameters' Bayesian estimates, a new methodology is developed, via simulation method of MCMC through the application of OpenBUGS platform. To demonstrate under the gamma and uniform sets of priors, a real data set is taken. The generations of posterior MCMC samples is conducted with OpenBUGS software. For analyzing the output of so generated MCMC samples, and studying the statistical properties, distribution's comparison tools and model validation the functions of R have been used. The credible interval and predicted of the reliability, hazard and modal parameters' values are also estimated. We have shown that Bayesian estimators are more efficient than classical estimators for any real data set.

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