Wyniki wyszukiwania - Biblioteka Nauki

1

A comparison study on a new five-parameter generalized Lindley distribution with its sub-models

100%

Tharshan R. , Wijekoon P.

Statistics in Transition new series

|

2020

|

tom 21

|

nr 2

89-117

EN

In recent years, modifications of the classical Lindley distribution have been considered by many authors. In this paper, we introduce a new generalization of the Lindley distribution based on a mixture of exponential and gamma distributions with different mixing proportions and compare its performance with its sub-models. The new distribution accommodates the classical Lindley, Quasi Lindley, Two-parameter Lindley, Shanker, Lindley distribution with location parameter, and Three-parameter Lindley distributions as special cases. Various structural properties of the new distribution are discussed and the size-biased and the lengthbiased are derived. A simulation study is conducted to examine the mean square error for the parameters by means of the method of maximum likelihood. Finally, simulation studies and some real-world data sets are used to illustrate its flexibility in terms of its location, scale and shape parameters.

2

Odd log-logistic generalised Lindley distribution with properties and applications

88%

Ranjbar V. , Eftekharian A. , Kharazmi O. , Alizadeh M.

|

tom 24

|

nr 4

71-92

EN

In this paper, a new three-parameter lifetime model, called the odd log-logistic generalised Lindley 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. Different methods are discussed to estimate the model parameters and a simulation study is carried out 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. Finally, Bayesian analysis and Gibbs sampling are performed based on the two real data sets.

3

On the characterisation of X-Lindley distribution by truncated moments. Properties and application

75%

METIRI F. , ZEGHDOUDI H. , EZZEBSA A.

Operations Research and Decisions

|

2022

|

tom 32

|

nr 1

97-109

EN

This paper presents the characterisation of X-Lindley distribution using the relation between truncated moment and failure rate/reverse failure rate function. An application of this distribution to real data of survival times (in days) of 92 Algerian individuals infected with coronavirus is given.

4

A Two-Parameter Lindley Distribution

75%

Shanker R. , Mishra A.

|

nr 1

45-56

EN

A two-parameter Lindley distribution, of which the Lindley distribution (LD) is a particular case, has been introduced. Its moments, failure rate function, mean residual life function and stochastic orderings have been discussed. The maximum likelihood method and the method of moments have been discussed for estimating its parameters. The distribution has been fitted to some data-sets to test its goodness of fit.

5

New polynomial exponential distribution: properties and applications

75%

Beghriche A. , Zeghdoudi H. , Raman V. , Chouia S.

|

tom 23

|

nr 3

95-112

EN

The study describes the general concept of the XLindley distribution. Forms of density and hazard rate functions are investigated. Moreover, precise formulations for several numerical properties of distributions are derived. Extreme order statistics are established using stochastic ordering, the moment method, the maximum likelihood estimation, entropies and the limiting distribution. We demonstrate the new family's adaptability by applying it to a variety of real-world datasets.

6

On the characterisation of X-Lindley distribution by truncated moments. Properties and application

75%

Metiri F. , Zeghdoudi H. , Ezzebsa A.

Operations Research and Decisions

|

2022

|

tom Vol. 32, No. 1

99--109

EN

Several papers introduce the new distributions and their applications, including, among others, those of Ducey and Gove [7], Grine and Zeghdoudi [8], Chouia et al. [5], Seghier et al. [11], Beghriche and Zeghdoudi [4], where characterisation of a probability distribution plays an important role in statistical science. Several researchers studied the characterisations of probability distributions. For example, Su and Huang [12] study the characterisations of distributions based on expectations. In addition, Nanda [10] studies the characterisations by average residual life and the failure rates of functions of absolutely continuous random variables. Ahmadi et al. [1] consider the estimation based on the left-truncated and right randomly censored data arising from a general family of distributions. On the other hand, Ahsanullah et al. [2, 3] present two characterisations of Lindley distribution, standard normal distribution, t-Student’s, exponentiated exponential, power function, Pareto, and Weibull distributions based on the relation of failure rate, reverse failure rate functions with left and right truncated moments. Recently, Haseeb and Yahia [9] studied truncated moments for two general classes of continuous distributions. In this paper, two characterisations of the X-Lindley distribution, introduced by Chouia and Zeghdoudi [5] have been studied. They are based on the failure, relation of the inverse failure rate functions with the left and right truncated moments, respectively. Section 2 gives some properties of X-Lindley distribution. Section 3 discusses the characterisation of general distribution by left truncated and failure rate function and then right truncated and reverse failure rate function. Section 4 studies the characterisation of X-Lindley distribution by using the relation between left/right truncated moment and failure/reverse failure rate function. Finally, an illustrative example of X-Lindley distribution with other one-parameter distributions is given to show the superiority and flexibility of this model.