(1976). Survival Distributions: Reliability Applications in the Biomedical Sciences. Technometrics: Vol. 18, No. 4, pp. 501-501.

Survival Distributions: Reliability Applications in the Biomedical Sciences

A new distribution is proposed for modeling lifetime data. It has better hazard rate properties than the gamma, lognormal and the Weibull distributions. A comprehensive account of the mathematical properties of the new distribution including estimation and simulation issues is presented. A real data example is discussed to illustrate its applicability.

https://www.jstor.org/stable/pdfplus/23885150.pdf

A generalized Lindley distribution

A new one parameter lifetime distribution named "Akash distribution" for modeling lifetime data has been introduced. Some important mathematical properties of the proposed distribution including its shape, moments, skewness, kurtosis, hazard rate function, mean residual life function, stochastic ordering, mean deviations, order statistics, Bonferroni and Lorenz curves, Renyi entropy measure, stress-strength reliability have been discussed. The condition under which Akash distribution is over-dispersed, equi-dispersed, and under-dispersed are presented along with the conditions under which exponential and Lindley distributions are over-dispersed, equi-dispersed and under-dispersed. The estimation of its parameter has been discussed using maximum likelihood estimation and method of moments. The usefulness and the applicability of the proposed distribution have been discussed and illustrated with two real lifetime data sets from medical science and engineering.

/pdf/akash-distribution-and-its-applications-46vq2grfxc.pdf

Akash Distribution and Its Applications

In this paper, firstly the nature of exponential and Lindley distributions have been studied using different graphs of their probability density functions and cumulative distribution functions. The expressions for the index of dispersion for both exponential and Lindley distributions have been obtained and the conditions under which the exponential and Lindley distributions are over-dispersed, equidispersed, and under-dispersed has been given. Several real lifetimes data-sets has been fitted using exponential and Lindley distributions for comparative study and it has been shown that in some cases exponential distribution provides better fit than the Lindley distribution whereas in other cases Lindley distribution provides better fit than the exponential distribution.

https://medcraveonline.com/BBIJ/BBIJ-02-00042.pdf

On Modeling of Lifetimes Data Using Exponential and

In this paper a new one parameter lifetime distribution named "Shanker distribution" for modeling lifetime data has been suggested. Various mathematical properties of the new distribution including its shape, moments, generating functions, hazard rate and mean residual life functions, stochastic ordering, order statistics, Renyi entropy measure, mean deviations, Bonferroni and Lorenz curves, and stress-strength reliability have been presented. The conditions of over-dispersion, equi-dispersion, and under-dispersion of Shanker distribution have been discussed. The method of maximum likelihood estimation and the method of moments have been discussed for estimating its parameter. The usefulness, applicability and superiority of the proposed distribution over exponential and Lindley distributions have been illustrated with three real lifetime data- sets.

/pdf/shanker-distribution-and-its-applications-13frnfkoic.pdf

Shanker Distribution and Its Applications

In this paper, a two-parameter Lindley distribution, of which the one parameter Lindley distribution (LD) is a particular case, for modeling waiting and survival times data has been introduced. Its moments, failure rate function, mean residual life function, and stochastic orderings have been discussed. It is found that the expressions for failure rate function mean residual life function and stochastic orderings of the two-parameter LD shows flexibility over one-parameter LD and exponential distribution. 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 relating to waiting times and survival times to test its goodness of fit to which earlier the one parameter LD has been fitted by others and it is found that to almost all these data-sets the two parameter LD distribution provides closer fits than those by the one parameter LD.

/pdf/a-two-parameter-lindley-distribution-for-modeling-waiting-5bnlkejmnq.pdf

A Two-Parameter Lindley Distribution for Modeling Waiting and Survival Times Data

In this paper some of the important mathematical properties including moment generating function, mean deviations, order statistics, Bonferroni and Lorenz curves, Renyi entropy and stress strength reliability of two-parameter Lindley distribution (TPLD) of Shanker & Mishra have been discussed. Its goodness of fit over exponential and Lindley distributions have been illustrated with some real lifetime data-sets and found that TPLD is preferable over exponential and Lindley distributions for modeling lifetime data-sets.

https://medcraveonline.com/BBIJ/BBIJ-03-00056.pdf

On two - parameter lindley distribution and its applications to model lifetime data

In this paper mathematical and statistical properties including moment generating function, mean deviations about mean and median, order statistics, Bonferroni and Lorenz curves, Renyi entropy and stress strength reliability of quasi Lindley distribution (QLD) introduced by Shanker and Mishra (2013 a) have been derived and discussed. The goodness of fit of QLD over exponential and Lindley distributions have been illustrated with five real lifetime data-sets and found that QLD provides better fit than exponential and Lindley distributions.

/pdf/on-quasi-lindley-distribution-and-its-applications-to-model-19ga1zhdd9.pdf

On Quasi Lindley Distribution and Its Applications to Model Lifetime Data

/pdf/janardan-distribution-and-its-application-to-waiting-times-4ge79fvrh5.pdf

Janardan Distribution and its Application to Waiting Times Data

This paper provides a new technique of selecting the pivot column and the source row in primal all-integer linear programming, by which the number of iterations has been reduced in going through the initial simplex table to the optimal simplex table.

Shambhu Sharma

Papers

A Two-Parameter Lindley Distribution for Modeling Waiting and Survival Times Data

On two - parameter lindley distribution and its applications to model lifetime data

On Quasi Lindley Distribution and Its Applications to Model Lifetime Data

Janardan Distribution and its Application to Waiting Times Data

New Technique for Solving Primal All-integer Linear Programming