Odd log-logistic generalised Lindley distribution with properties and applications

In this paper, a new three-parameter lifetime model, called the new odd log-logistic Lindley (NOLL-L) distribution, is introduced. Some structural properties of the new distribution including ordinary and incomplete moments, quantile and generating functions and order statistics are obtained. The new density function can be expressed as a linear mixture of exponentiated Lindley densities. The different methods are discussed to estimate the model parameters and a simulation study is done to show the performance of the new distribution. The importance and flexibility of the new model are also illustrated empirically by means of two real data sets.

In this paper, a new three-parameter distribution called the Alpha Logarithm Transformed Fr\'{e}chet (ALTF) distribution is introduced which offers a more flexible distribution for modeling lifetime data. Various properties of the proposed distribution, including explicit expressions for the quantiles, moments, incomplete moments, conditional moments, moment generating function R\'{e}nyi and $\delta$-entropies, stochastic ordering, stress-strength reliability and order statistics are derived. The new distribution can have decreasing, reversed J-shaped and upside-down bathtub failure rate functions depending on its parameter values. The maximum likelihood method is used to estimate the distribution parameters. A simulation study is conducted to evaluate the performance of the maximum likelihood estimates. Finally, the proposed extended model is applied on real data sets and the results are given which illustrate the superior performance of the ALTF distribution compared to some other well-known distributions.

This paper introduces a new lifetime distribution, the so-called odd log-logistic exponentiated Weibull distribution. The new distribution has the advantage of being capable of modeling various shapes of aging and failure criteria. We derive various of its structural properties including ordinary and incomplete moments, quantile and generating functions, and order statistics. The new density function can be expressed as a linear mixture of exponentiated Weibull densities. We propose a log-linear regression model using a new distribution, the log-odd log-logistic exponentiated Weibull distribution. The maximum-likelihood method is used to estimate the model parameters. Simulation results to assess the performance of the maximum-likelihood estimation are discussed. We prove empirically the importance and flexibility of the new model by modeling four real data sets.

We study some mathematical properties of a new generator of continuous distributions with two extra parameters called the exponentiated half-logistic family. We present some special models. We investigate the shapes of the density and hazard rate function. We derive explicit expressions for the ordinary and incomplete moments, quantile and generating functions, probability weighted moments, Bonferroni and Lorenz curves, Shannon and Rényi entropies, and order statistics, which hold for any baseline model. We introduce two bivariate extensions of this family. We discuss the estimation of the model parameters by maximum likelihood and demonstrate the potentiality of the new family by means of two real data sets.

We propose a new family of continuous distributions called the odd generalized exponential family, whose hazard rate could be increasing, decreasing, J, reversed-J, bathtub and upside-down bathtub. It includes as a special case the widely known exponentiated-Weibull distribution. We present and discuss three special models in the family. Its density function can be expressed as a mixture of exponentiated densities based on the same baseline distribution. We derive explicit expressions for the ordinary and incomplete moments, quantile and generating functions, Bonferroni and Lorenz curves, Shannon and Renyi entropies and order statistics. For the first time, we obtain the generating function of the Frechet distribution. Two useful characterizations of the family are also proposed. The parameters of the new family are estimated by the method of maximum likelihood. Its usefulness is illustrated by means of two real lifetime data sets. AMS Subject Classification Primary 60E05; secondary 62N05; 62F10

We introduce a new model named the Kumaraswamy Pareto IV distribution which extends the Pareto and Pareto IV distributions. The density function is very flexible and can be left-skewed, right-skewed and symmetrical shapes. It hasincreasing, decreasing, upside-down bathtub, bathtub, J and reversed-J shaped hazard rate shapes. Various structural properties are derived including explicit expressions for the quantile function, ordinary and incomplete moments,Bonferroni and Lorenz curves, mean deviations, mean residual life, mean waiting time, probability weighted moments and generating function. We provide the density function of the order statistics and their moments. The Renyi and q entropies are also obtained. The model parameters are estimated by the method of maximum likelihood and the observed information matrix is determined. The usefulness of the new model is illustrated by means of three real-life data sets. In fact, our proposed model provides a better fit to these data than the gamma-Pareto IV, gamma-Pareto, beta-Pareto,exponentiated Pareto and Pareto IV models.

We define and study a new generalization of the Fréchet distribution called the beta exponential Fréchet distribution. The new model includes thirty two special models. Some of its mathematical properties, including explicit expressions for the ordinary and incomplete moments, quantile and generating functions, mean residual life, mean inactivity time, order statistics and entropies are derived. The method of maximum likelihood is proposed to estimate the model parameters. A small simulation study is alsoreported. Two real data sets are applied to illustrate the flexibility of the proposed model compared with some nested and non-nested models.

<h2>Abstract</h2>\n<p><strong>Objective:</strong> To find appropriate model more flexible than classical models for fitting the survival data in health research especially in cancer blood (Leukemia) studies by introducing some additional parameters to the basic models.<strong> Methods:</strong> Based on the Alpha Power transformation technique and using Two-Parameter Odoma distribution, the new distribution called the Alpha Power Two- Parameter Odoma distribution is introduced and studied. The maximum likelihood estimation procedure is employed to estimate the unknown parameters. A simulation study is carried out to evaluate the performance of the maximum likelihood estimators. <strong>Finding:</strong> Common statistical properties such as quantile function, rth moments, moment generating function, characteristic function, incomplete moments, entropy and order statistics are derived. The practical importance of the proposed model is illustrated by using goodness-of-fit criterias based on real data set.  The APTPO distribution is the most appropriate model for fitting survival data <strong>Novelty:</strong>of cancer studies comparing with other competitive distributions.</p>\n<p><strong>Keywords:</strong> TwoParameter Odoma Distribution; Alpha Power Transformation; Quantile; Moments; Maximum Likelihood Estimation</p>

We introduce a new three-parameter extension of the exponential distribution called the odd exponentiated half-logistic exponential distribution. Various of its properties including quantile and generating functions, ordinary and incomplete moments, mean residual life, mean inactivity time and some characterizations are investigated. The new density function can be expressed as a linear mixture of exponential densities. The hazard rate function of the new model can be decreasing, increasing or bathtub shaped. The maximum likelihood is used for estimating the model parameters. We prove empirically the flexibility of the new distribution using two real data sets.

The transmuted family of distributions has been receiving increased attention over the last few years. In this paper, we generalize the Marshall-Olkin extended Lomax distribution using the quadratic rank transmutation map to obtain the transmuted Marshall-Olkin extended Lomax distribution. Several properties of the new distribution are discussed including the hazard rate function, ordinary and incomplete moments, characteristic function and order statistics. We provide an estimation procedure by the maximum likelihood method and a simulation study to assess the performance of the new distribution. We prove empirically the flexibility of the new model by means of an application to a real data set. It is superior to other three and four parameter lifetime distributions.

A new univariate extension of the Fréchet distribution is proposed and studied. Some of its fundamental statistical properties such as stochastic properties, ordinary and incomplete moments, moments generating functions, residual life and reversed residual life functions, order statistics, quantile spread ordering, Rényi, Shannon and q-entropies are derived. A simple type Copula based construction using Morgenstern family and via Clayton Copula is employed to derive many bivariate and multivariate extensions of the new model. We assessed the performance of the maximum likelihood estimators using a simulation study. The importance of the new model is shown by means of two applications to real data sets.

A new lifetime model, which extends the Weibull distribution using the generalized transmuted-G family proposed by Nofal et al. (2016), called the generalized transmuted Weibull distribution is proposed and studied. Various of its structural properties including ordinary and incomplete moments, quantile and generating functions, probability weighted moments, ROnyi and q-entropies and order statistics are derived. The maximum likelihood method is used to estimate the model parameters. The new distribution is applied to two real data sets to illustrate its ‡exibility. It can serve as an alternative model to other lifetime models available in the literature for modeling positive real data in many areas.

ABSTRACTThis paper introduces a new four-parameter lifetime model called the Weibull Burr XII distribution. The new model has the advantage of being capable of modeling various shapes of aging and failure criteria. We derive some of its structural properties including ordinary and incomplete moments, quantile and generating functions, probability weighted moments, and order statistics. The new density function can be expressed as a linear mixture of Burr XII densities. We propose a log-linear regression model using a new distribution so-called the log-Weibull Burr XII distribution. The maximum likelihood method is used to estimate the model parameters. Simulation results to assess the performance of the maximum likelihood estimation are discussed. We prove empirically the importance and flexibility of the new model in modeling various types of data.

A new distribution defined on (0,1) interval is introduced. Its probability density and cumulative distribution functions have simple forms. Thanks to its simple forms, the moments, incomplete moments and quantile function of the proposed distribution are derived and obtained in explicit forms. Four parameter estimation methods are used to estimate the unknown parameter of the distribution. Besides, simulation study is implemented to compare the efficiencies of these parameter estimation methods. More importantly, owing to the proposed distribution, we provide an alternative regression model for the bounded response variable. The proposed regression model is compared with the beta and unit-Lindley regression models based on two real data sets.

We introduce and study general mathematical properties of a new generator of continuous distributions with two extra parameters called the Generalized transmuted family of distributions. We present some special models. We investigate the asymptotes and shapes. The new density function can be expressed as a linear combination of exponentiated densities based on the same baseline distribution. We obtain explicit expressions for the ordinary and incomplete moments and generating functions, Bonferroni and Lorenz curves, asymptotic distribution of the extreme values, Shannon and Renyi entropies and order statistics, which hold for any baseline model, certain characterisations are presented. Further, we introduce a bivariate extensions of the new family. We discuss the different method of estimation of the model parameters and illustrate the potentiality of the family by means of two applications to real data. A brief simulation for evaluating Maximum likelihood estimator is done.