A Three Parameter Generalized Lindley Distribution: Properties and Application

In this paper, we propose the truncated version of the power Lindley distribution which is more flexible than many well-known distributions. Mathematical and statistical properties of truncated power Lindley distribution are given only for upper truncated version of power Lindley distribution. The statistical proprieties such as moments, quantile function and order statistics are also discussed. The maximum likelihood estimators are constructed for estimating the unknown parameters of the upper, lower and double truncated power Lindley distributions. A set of real data containing the strengths of the glass of aircraft window, is considered to show the applicability of the truncated power Lindley distributions. The real data set is considered to illustrate the utility and potential of the proposed model.

In this paper, we introduce a new class of distributions by compounding the exponentiated extended Weibull family and power series family. This distribution contains several lifetime models such as the complementary extended Weibull-power series, generalized exponential-power series, generalized linear failure rate-power series, exponentiated Weibull-power series, generalized modifiedWeibull-power series, generalized Gompertz-power series and exponentiated extendedWeibull distributions as special cases. We obtain several properties of this new class of distributions such as Shannon entropy, mean residual life, hazard rate function, quantiles and moments. The maximum likelihood estimation procedure via a EM-algorithm is presented.

The new distributions are very useful in describing real data sets, because these distributions are more flexible to model real data that present a high degree of skewness and kurtosis. The choice of the best-suited statistical distribution for modeling data is very important.In this paper, A new class of distributions called the {\it New odd log-logistic generalized half-normal} (NOLL-GHN) family with four parameters is introduced and studied. This model contains sub-models such as half-normal (HN), generalized half-normal (GHN )and odd log-logistic generalized half-normal (OLL-GHN) distributions.some statistical properties such as moments and moment generating function have been calculated.The Biases and MSE's of estimator methods such as maximum likelihood estimators , least squares estimators, weighted least squares estimators,Cramer-von-Mises estimators, Anderson-Darling estimators and right tailed Anderson-Darling estimators are calculated.The fitness capability of this model has been investigated by fitting this model and others based on real data sets. The maximum likelihood estimators are assessed with simulated real data from proposed model. We present the simulation in order to test validity of maximum likelihood estimators.

The present paper introduces a new model namely length biased power Lindley distribution with two parameters. The survival function, failure rate and moments of the proposed distribution are derived. The maximum likelihood estimators of the parameters and their asymptotics are discussed and also the order statistics are derived. The potentiality of the proposed model has been tested by using two real life time data sets.

ABSTRACTThe Lindley distribution is an important distribution for analysing the stress–strength reliability models and lifetime data. In many ways, the Lindley distribution is a better model than that based on the exponential distribution. Order statistics arise naturally in many of such applications. In this paper, we derive the exact explicit expressions for the single, double (product), triple and quadruple moments of order statistics from the Lindley distribution. Then, we use these moments to obtain the best linear unbiased estimates (BLUEs) of the location and scale parameters based on Type-II right-censored samples. Next, we use these results to determine the mean, variance, and coefficients of skewness and kurtosis of some certain linear functions of order statistics to develop Edgeworth approximate confidence intervals of the location and scale Lindley parameters. In addition, we carry out some numerical illustrations through Monte Carlo simulations to show the usefulness of the findings. Finally, we apply the findings of the paper to some real data set.

A two-parameter Quasi Lindley distribution (QLD), 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. It is found that the expressions for failure rate function, mean residual life function, and stochastic orderings of the QLD shows its flexibility over Lindley distribution and exponential distribution. Although, the QLD has two parameters, the expressions for coefficients of variation, skewness, and kurtosis depend upon only one parameter. 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 to which earlier the Lindley distribution has been fitted by others and it is found that to almost all these data-sets the QLD provides closer fits than those by the Lindley distribution. Key words: Lindley distribution, moments, failure rate function, mean residual life function, stochastic ordering, estimation of parameters, goodness of fit. .

This paper introduces a new class of skew distributions by extending the alpha skew normal distribution proposed by Elal-Olivero [Elal-Olivero, D. Alpha-skew-normal distribution. Proyecciones. 2010;29:224–240]. Statistical properties of the new family are studied in details. In particular, explicit expressions for the moments and the shape parameters including the skewness and the kurtosis coefficients and the moment generating function are derived. The problem of estimating parameters on the basis of a random sample coming from the new class of distribution is considered. To examine the performance of the obtained estimators, a Monte Carlo simulation study is conducted. Flexibility and usefulness of the proposed family of distributions are illustrated by analysing three real data sets.

A study on two-parameter power Ishita distribution (PID), of which Ishita distribution introduced by Shanker and Shukla (2017 a) is a special case, has been carried out and its important statistical properties including shapes of the density, moments, skewness and kurtosis measures, hazard rate function, and stochastic ordering have been discussed. The maximum likelihood estimation has been discussed for estimating its parameters. An application of the distribution has been explained with a real lifetime data from engineering, and its goodness of fit shows better fit over two-parameter power Akash distribution (PAD), two-parameter power Lindley distribution (PlD) and one-parameter Ishita, Akash, Lindley and exponential distributions.

ABSTRACTIn the present article we introduce a new class of distributions which nests the classical Logistic distribution and offers additional flexibility when data fitting is chased. We provide exact expressions for its moments and absolute moments, investigate its ageing properties, and discuss several techniques for estimating its parameters. Finally, we use the new family to build a parametric model that describes accurately the Euro/CAD exchange reference rates for the period 1/4/1999–12/31/2011.

A new class of weighted distributions is proposed by incorporating an extended exponential distribution in Azzalini’s (1985) method. Several statistics and reliability properties of this new class of distribution are obtained. Maximum likelihood estimators of the unknown parameters cannot be obtained in explicit forms; they have to be obtained by solving some numerical methods. Two data sets are analyzed for illustrative purposes, and show that the proposed model can be used effectively in analyzing real data.

In this article, a new class of distribution in the exponential family of distributions called the Gompertz extended generalized exponential distribution (G-EGE) for life time processes is proposed. The mathematical properties of the G-EGE distribution such as the reliability, hazard rate function, reversed hazard, cumulative. odd functions, quantiles function, kurtosis, skewness and order statistics were derived. The parameter estimates of the G-EGE distribution were estimated using the maximum likelihood estimation method. The efficiency and flexibility of the G-EGE distribution were examined using a simulation study and a real life data application. The results revealed that the G-EGE distribution outperformed some of the existing distributions considered in this study.

A new class of distributions called the log-logistic Weibull–Poisson distribution is introduced and its properties are explored. This new distribution represents a more flexible model for lifetime data. Some statistical properties of the proposed distribution including the expansion of the density function, quantile function, hazard and reverse hazard functions, moments, conditional moments, moment generating function, skewness and kurtosis are presented. Mean deviations, Bonferroni and Lorenz curves, Rényi entropy and distribution of the order statistics are derived. Maximum likelihood estimation technique is used to estimate the model parameters. A simulation study is conducted to examine the bias, mean square error of the maximum likelihood estimators and width of the confidence interval for each parameter and finally applications of the model to real data sets are presented to illustrate the usefulness of the proposed distribution.

In this paper, we have developed single and double acceptance sampling plans when the product life length follows the power Lindley distribution. The sampling plans have been developed by assuming infinite and finite lot sizes. We have obtained the operating characteristic curves for the resultant sampling plans. The sampling plans have been obtained for various values of the parameters. It has been found that for a finite lot size, the sampling plans provide smaller values of the parameters to achieve the specified acceptance probabilities.

There is an increasing interest in expanding the one‐parameter Lindley distribution to two‐parameter, three‐parameter, and five‐parameter. The univariate one‐parameter Lindley distribution is still one of the most applicable distributions in data analysis especially in lifetime data. Modeling dependent random quantities required bivariate parametric probability distributions. This study presents a new bivariate three‐parameter probability distribution called bivariate modified Lindley distribution. The one‐parameter modified Lindley distribution is used as a base line to construct the new model. Its statistical properties including cumulative function, density function, marginals, moments, conditional distributions, and copula are discussed. Simulation is constructed to declare theoretical properties, to show the flexibility of the new model and to investigate the goodness of fit. Two sets of real data, financial data and UEFA Champion’s League data, are used to show the applicability of the proposed model for different types of data.

In this article we have proposed a general transmuted family of distributions with emphasis on the cubic transmuted family of distributions. This new class of distributions provide additional exibility in modeling the bi-modal data. The proposed cubic transmuted family of distributions has been linked with the T-X family of distributions proposed by Alzaatreh et al. (2013). Some members of the proposed family of distributions have been discussed. The cubic transmuted exponential distribution has been discussed in detail and various statistical properties of the distribution have been explored. The maximum likelihood estimation for parameters of cubic transmuted exponential distribution has also been discussed alongside Monte Carlo simulation study to assess the performance of the estimation procedure. Finally, the cubic transmuted exponential distribution has been tted to real datasets to investigate its applicability.