The sine extended odd Fréchet-G family of distribution with applications to complete and censored data

2021 ◽  
Vol 71 (4) ◽  
pp. 961-982
Author(s):  
Farrukh Jamal ◽  
Christophe Chesneau ◽  
Khaoula Aidi
Keyword(s):  
Censored Data ◽  
Statistical Tests ◽  
Quantile Function ◽  
Likelihood Method ◽  
Right Censored Data ◽  
Mathematical Properties ◽  
Linear Representations ◽  
Proposed Model ◽  
The Family ◽  
The Right

Abstract In this paper, we introduce the sine extended odd Fréchet-G family of distributions, obtained from two well-established families of distributions of completely different nature: the sine-G and the extended odd Fréchet-G families. A particular focus is put on a very flexible member of this family defin ed with the Nadarajah-Haghighi distribution as a baseline, called the sine extended odd Fréchet Nadarajah-Haghighi distribution. For the theoretical part, the interesting mathematical properties of the family are investigated, including asymptotes, quantile function, linear representations and moments, with application to the introduced special member. Then, the inferential aspects of the sine extended odd Fréchet Nadarajah-Haghighi model are examined. In particular, the parameters are estimated by the maximum likelihood method. Two complementary cases are distinguished: the complete data case and the right censored data case, with the development of appropriate statistical tests. A simulation study is carried out to illustrate the convergence of the obtained estimates. Applications are given for three practicaldata sets, including one having the right censored property, illustrating the applicability of the proposed model.

MATHEMATICAL PROPERTIES AND APPLICATIONS OF MINIMUM GUMBEL BURR DISTRIBUTION

2020 ◽  
Vol XVII (2) ◽  
pp. 1-14
Author(s):  
Farrukh Jamal ◽  
Hesham Mohammed Reyad ◽  
Soha Othman Ahmed ◽  
Syed Muhammad Akbar Ali Shah
Keyword(s):  
Stochastic Ordering ◽  
Quantile Function ◽  
Continuous Model ◽  
Moment Generating Function ◽  
Likelihood Method ◽  
Parameter Estimates ◽  
Mathematical Properties ◽  
Proposed Model ◽  
Burr Distribution ◽  
New Distribution

This paper presents the details of a proposed continuous model for the minimum Gumbel Burr distribution which is based on four different parameters. The model is obtained by compounding the Gumbel type-II and Burr-XII distributions. Basic mathematical properties of the new distribution were studied including the quantile function, ordinary and incomplete moments, moment generating function, order statistics, Rényi entropy, stress-strength model and stochastic ordering. The parameters of the proposed distribution are estimated using the maximum likelihood method. A Monte Carlo simulation was presented to examine the behaviour of the parameter estimates. The flexibility of the proposed model was assessed by means of three applications.

MATHEMATICAL PROPERTIES AND APPLICATIONS OF MINIMUM GUMBEL BURR DISTRIBUTION

2020 ◽  
Vol XVII (2) ◽  
pp. 1-14
Author(s):  
Farrukh Jamal ◽  
Mohammed Reyad ◽  
Soha Othman Ahmed ◽  
Syed Muhammad Akbar Ali Shah
Keyword(s):  
Stochastic Ordering ◽  
Quantile Function ◽  
Continuous Model ◽  
Moment Generating Function ◽  
Likelihood Method ◽  
Parameter Estimates ◽  
Mathematical Properties ◽  
Proposed Model ◽  
Burr Distribution ◽  
New Distribution

This paper presents the details of a proposed continuous model for the minimum Gumbel Burr distribution which is based on four different parameters. The model is obtained by compounding the Gumbel type-II and Burr-XII distributions. Basic mathematical properties of the new distribution were studied including the quantile function, ordinary and incomplete moments, moment generating function, order statistics, Rényi entropy, stress-strength model and stochastic ordering. The parameters of the proposed distribution are estimated using the maximum likelihood method. A Monte Carlo simulation was presented to examine the behaviour of the parameter estimates. The flexibility of the proposed model was assessed by means of three applications.

The Poisson Nadarajah–Haghighi Distribution: Properties and Applications to Lifetime Data

2019 ◽  
Vol 27 (01) ◽  
pp. 2050005
Author(s):  
Muhammad Mansoor ◽  
M. H. Tahir ◽  
Aymaan Alzaatreh ◽  
Gauss M. Cordeiro
Keyword(s):  
Maximum Likelihood ◽  
Censored Data ◽  
Survival Data ◽  
Maximum Likelihood Method ◽  
Likelihood Estimation ◽  
Real Data ◽  
Likelihood Method ◽  
Lifetime Data ◽  
Monte Carlo Simulation Study ◽  
Mathematical Properties

A new three-parameter compounded extended-exponential distribution “Poisson Nadarajah–Haghighi” is introduced and studied, which is quite flexible and can be used effectively in modeling survival data. It can have increasing, decreasing, upside-down bathtub and bathtub-shaped failure rate. A comprehensive account of the mathematical properties of the model is presented. We discuss maximum likelihood estimation for complete and censored data. The suitability of the maximum likelihood method to estimate its parameters is assessed by a Monte Carlo simulation study. Four empirical illustrations of the new model are presented to real data and the results are quite satisfactory.

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.

scholarly journals Discrete Gompertz-G Family of Distributions for Over- and Under-Dispersed Data with Properties, Estimation, and Applications

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 358 ◽  
Author(s):  
M. S. Eliwa ◽  
Ziyad Ali Alhussain ◽  
M. El-Morshedy
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Discrete Analogue ◽  
Likelihood Method ◽  
New Family ◽  
Distributional Properties ◽  
Reliability Characteristics ◽  
Proposed Model ◽  
The Family ◽  
Family Of Distributions

Alizadeh et al. introduced a flexible family of distributions, in the so-called Gompertz-G family. In this article, a discrete analogue of the Gompertz-G family is proposed. We also study some of its distributional properties and reliability characteristics. After introducing the general class, three special models of the new family are discussed in detail. The maximum likelihood method is used for estimating the family parameters. A simulation study is carried out to assess the performance of the family parameters. Finally, the flexibility of the new family is illustrated by means of four genuine datasets, and it is found that the proposed model provides a better fit than the competitive distributions.

Research on a Kind of Distribution Selection Method for Right Censored Data Based on Data Expansion Algorithm

2012 ◽  
Vol 605-607 ◽  
pp. 2257-2259
Author(s):  
Jin Huang Wu ◽  
Hai Bo Liu ◽  
Yi Dong Wang ◽  
Jun Wei Lei
Keyword(s):  
Censored Data ◽  
Data Distribution ◽  
Reliability Assessment ◽  
Selection Method ◽  
Parameter Distribution ◽  
Reliability Engineering ◽  
Right Censored Data ◽  
Reasonable Choice ◽  
The Right ◽  
Data Expansion

A kind of distribution selection method is researched for the right censored data situation. It can be widely applied in life sciences, medicine and health, reliability engineering, aerospace , social sciences and other related fields. By using a common PL estimation curve as a basic curve, a kind of distribution selection method is proposed to solve the parameter distribution problem of right censored data. This method can meet the selection requirement of data distribution in reliability assessment and other data analysis. It can guide the engineer and technician to choose a reasonable choice of the fitting of the data distribution, and it can improve the accuracy and efficiency of the reliability assessment.

scholarly journals The Ristic-Balakrishnan odd log-logistic family of distributions: Properties and Applications

2020 ◽  
Vol 8 (1) ◽  
pp. 17-35
Author(s):  
Hamid Esmaeili ◽  
Fazlollah Lak ◽  
Emrah Altun
Keyword(s):  
Maximum Likelihood ◽  
Likelihood Estimation ◽  
Real Data ◽  
Model Parameters ◽  
Mathematical Properties ◽  
Proposed Model ◽  
The Family ◽  
Logistic Family ◽  
Family Of Distributions ◽  
Extra Parameter

This paper investigates general mathematical properties of a new generator of continuous distributions with two extra parameter called the Ristic-Balakrishnan odd log-logistic family of distributions. We present some special models and investigate the asymptotes. The new density function can be expressed as a linear combination of exponentiated densities based on the same baseline distribution. Explicit expressions for the ordinary and incomplete moments, generating functions and order statistics, which hold for any baseline model, are determined. Further, we discuss the estimation of the model parameters by maximum likelihood and present a simulation study based on maximum likelihood estimation. A regression model based on proposed model was introduced. Finally, three applications to real data were provided to illustrate the potentiality of the family of distributions.

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.

scholarly journals A Modified Chi-square Type Test Statistic for the Double Burr X Model with Applications to Right Censored Medical and Reliability Data

2021 ◽  
pp. 615-623
Author(s):  
Khaoula Aidi ◽  
Nadeem Shafique Butt ◽  
Mir Masoom Ali ◽  
Mohamed Ibrahim ◽  
Haitham M. Yousof ◽  
...  
Keyword(s):  
Censored Data ◽  
Goodness Of Fit ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Data Sets ◽  
Test Statistic ◽  
Goodness Of Fit Test ◽  
Chi Square ◽  
Right Censored Data ◽  
The Right

A new modified version of the Bagdonavičius-Nikulin goodness-of-fit test statistic is presented for validity for the right censor case under the double Burr type X distribution. The maximum likelihood estimation method in censored data case is used and applied. Simulations via the algorithm of Barzilai-Borwein is performed for assessing the right censored estimation method. Another simulation study is presented for testing the null hypothesis under the modified version of the Bagdonavičius and Nikulin goodness-of-fit statistical test. Four right censored data sets are analyzed under the new modified test statistic for checking the distributional validation.

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