The Generalized Weighted Lindley Distribution: Properties, Estimation and Applications

In this paper, we proposed a new lifetime distribution namely generalized weighted Lindley (GLW) distribution. The GLW distribution is a useful generalization of the weighted Lindley distribution, which accommodates increasing, decreasing, decreasing-increasing-decreasing, bathtub, or unimodal hazard functions, making the GWL distribution a flexible model for reliability data. A significant account of mathematical properties of the new distribution are presented. Different estimation procedures are also given such as, maximum likelihood estimators, method of moments, ordinary and weighted least-squares, percentile, maximum product of spacings and minimum distance estimators. The different estimators are compared by an extensive numerical simulations. Finally, we analyze two data sets for illustrative purposes, proving that the GWL outperform several usual three parameters lifetime distributions

Exact solution to a Lindley-type equation on a bounded support

We derive the limiting waiting-time distribution $F_W$ of a model described by the Lindley-type equation $W=\max\{0, B - A - W\}$, where $B$ has a polynomial distribution. This exact solution is applied to derive approximations of $F_W$ when $B$ is generally distributed on a finite support. We provide error bounds for these approximations.Comment: 9 pages, 2 figures, 1 table, 11 reference

A new two parameter lifetime distribution: model and properties

In this paper a new lifetime distribution which is obtained by compounding Lindley and geometric distributions, named Lindley-geometric (LG) distribution, is introduced. Several properties of the new distribution such as density, failure rate, mean lifetime, moments, and order statistics are derived. Furthermore, estimation by maximum likelihood and inference for large sample are discussed. The paper is motivated by two applications to real data sets and we hope that this model be able to attract wider applicability in survival and reliability.Comment: arXiv admin note: text overlap with arXiv:1007.023

On queues with service and interarrival times depending on waiting times

We consider an extension of the standard G/G/1 queue, described by the equation $W\stackrel{\mathcal{D}}{=}\max\{0, B-A+YW\}$, where $\mathbb{P}[Y=1]=p$ and $\mathbb{P}[Y=-1]=1-p$. For $p=1$ this model reduces to the classical Lindley equation for the waiting time in the G/G/1 queue, whereas for $p=0$ it describes the waiting time of the server in an alternating service model. For all other values of $p$ this model describes a FCFS queue in which the service times and interarrival times depend linearly and randomly on the waiting times. We derive the distribution of $W$ when $A$ is generally distributed and $B$ follows a phase-type distribution, and when $A$ is exponentially distributed and $B$ deterministic.Comment: 19 pages, 1 figure, 24 reference

An alternating service problem

We consider a system consisting of a server alternating between two service points. At both service points there is an infinite queue of customers that have to undergo a preparation phase before being served. We are interested in the waiting time of the server. The waiting time of the server satisfies an equation very similar to Lindley's equation for the waiting time in the GI/G/1 queue. We will analyse this Lindley-type equation under the assumptions that the preparation phase follows a phase-type distribution while the service times have a general distribution. If we relax the condition that the server alternates between the service points, then the model turns out to be the machine repair problem. Although the latter is a well-known problem, the distribution of the waiting time of the server has not been studied yet. We shall derive this distribution under the same setting and we shall compare the two models numerically. As expected, the waiting time of the server is on average smaller in the machine repair problem than in the alternating service system, but they are not stochastically ordered.Comment: 15 pages, 7 figures, 14 reference

On the one parameter unit-Lindley distribution and its associated regression model for proportion data

In this paper considering the transformation $X=\frac{Y}{1+Y}$, where $Y \sim\text{Lindley}(\theta)$, we propose the unit-Lindley distribution and investigate some of its mathematical properties. A important fact associated with this new distribution is that is possible to obtain the analytical expression for bias correction of the maximum likelihood estimator. Moreover, it belongs to the exponential family. This distribution allows us to incorporate covariates directly in the mean and consequently to quantify the influence on the average of the response variable. Finally, a practical application is present and it is shown that our model fits much better than the Beta regression.Comment: 17 pages, 4 figure

The Utility of Reliability and Survival

Reliability (survival analysis, to biostatisticians) is a key ingredient for mak- ing decisions that mitigate the risk of failure. The other key ingredient is utility. A decision theoretic framework harnesses the two, but to invoke this framework we must distinguish between chance and probability. We describe a functional form for the utility of chance that incorporates all dispositions to risk, and pro- pose a probability of choice model for eliciting this utility. To implement the model a subject is asked to make a series of binary choices between gambles and certainty. These choices endow a statistical character to the problem of utility elicitation. The workings of our approach are illustrated via a live example in- volving a military planner. The material is general because it is germane to any situation involving the valuation of chance

Some estimators of the PDF and CDF of the Lindley Distribution

This article addresses the different methods of estimation of the probability density function (PDF) and the cumulative distribution function (CDF) for the Lindley distribution. Following estimation methods are considered: uniformly minimum variance unbiased estimator (UMVUE), maximum likelihood estimator (MLE), percentile estimator (PCE), least square estimator (LSE), weighted least square estimator (WLSE), Cram\'{e}r-von-Mises estimator (CVME), Anderson-Darling estimator (ADE). Monte Carlo simulations are performed to compare the performances of the proposed methods of estimation

Maximum Likelihood Estimation for the Weight Lindley Distribution Parameters under Different Types of Censoring

In this paper the maximum likelihood equations for the parameters of the Weight Lindley distribution are studied considering different types of censoring, such as, type I, type II and random censoring mechanism. A numerical simulation study is perform to evaluate the maximum likelihood estimates. The proposed methodology is illustrated in a real data set.Comment: 19 pg

A new probability model with support on unit interval: Structural properties, regression of bounded response and applications

A new distribution on (0, 1), generalized Log-Lindley distribution, is proposed by extending the Log-Lindley distribution. This new distribution is shown to be a weighted Log-Lindley distribution. Important probabilistic and statistical properties have been derived. An interesting characterization of the weighted distribution in terms of Kullback-Liebler distance and weighted entropy has also been obtained. A useful result in insurance for the distorted premium principal is presented and verified with numerical calculations. New regression models for bounded responses based on this distribution and their application is illustrated by considering modeling a real life data on risk management and another data set on outpatient health expenditure in comparison with beta regression and Log-Lindley regression models. Much better fits for both data sets justify the relevance of the new distribution in statistical modeling and analysis. Furthermore this generalization, apart from adding flexibility for modelling, retains the compactness and tractability of statistical quantities required for statistical analysis, which is a feature of the Log-Lindley distribution. Thus, the generalized Log-Lindley distribution should be a useful addition to statistical models for practitioners.Comment: 37 pages; 2 Figures, 3 Table, Pre-print Version-4.