A New Generalized Two Parameter Lindley Distribution

Ekhosuehi, N.; Opone, Festus C.; Odobaire, F.

doi:10.6339/jds.201807_16(3).0006

Cited by 9 publications

(8 citation statements)

References 8 publications

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“…[8] studied the mathematical properties of the Lindley distribution in detail and applied it to a waiting time dataset, reigniting the interest of researchers. This sparked the introduction of several modifications of the Lindley distribution, including the extended Lindley distribution introduced by [9], the Lindley-Exponential distribution studied by [10], a generalized two-parameter Lindley distribution treated by [11], a three-parameter generalized Lindley distribution proposed by [12], the Marshall-Olkin generalized Lindley distribution studied by [13], and the new alpha power transformed power Lindley distribution developed by [14]. One relevant modification of the Lindley distribution is the power Lindley distribution developed by [15].…”

Section: Lifetime Distributions Have Become Increasingly Popular In S...mentioning

confidence: 99%

Exploring a New Lifetime Distribution for Modelling the Waiting Time of Bank Customers

Ogumeyo,

Ehiwario,

Opone

2024

JAMP

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The fitting of lifetime distribution in real-life data has been studied in various fields of research. With the theory of evolution still applicable, more complex data from real-world scenarios will continue to emerge. Despite this, many researchers have made commendable efforts to develop new lifetime distributions that can fit this complex data. In this paper, we utilized the KM-transformation technique to increase the flexibility of the power Lindley distribution, resulting in the Kavya-Manoharan Power Lindley (KMPL) distribution. We study the mathematical treatments of the KMPL distribution in detail and adapt the widely used method of maximum likelihood to estimate the unknown parameters of the KMPL distribution. We carry out a Monte Carlo simulation study to investigate the performance of the Maximum Likelihood Estimates (MLEs) of the parameters of the KMPL distribution. To demonstrate the effectiveness of the KMPL distribution for data fitting, we use a real dataset comprising the waiting time of 100 bank customers. We compare the KMPL distribution with other models that are extensions of the power Lindley distribution. Based on some statistical model selection criteria, the summary results of the analysis were in favor of the KMPL distribution. We further investigate the density fit and probability-probability (p-p) plots to validate the superiority of the KMPL distribution over the competing distributions for fitting the waiting time dataset.

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Section: Lifetime Distributions Have Become Increasingly Popular In S...mentioning

confidence: 99%

Exploring a New Lifetime Distribution for Modelling the Waiting Time of Bank Customers

Ogumeyo,

Ehiwario,

Opone

2024

JAMP

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“…This distribution was recently introduced by Altun [ 8 ] and is a Poisson mixture when the Poisson parameter follows the new generalized Lindley distribution studied by [ 20 ] with m.g.f. Some properties of this distribution are: …”

Section: Univariate Poisson Mixtures and Poisson–lindley Distributionsmentioning

confidence: 99%

Bivariate Discrete Poisson–Lindley Distributions

Papageorgiou

Vardaki

2022

J Stat Theory Pract

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Two families of bivariate discrete Poisson–Lindley distributions are introduced. The first is derived by mixing the common parameter in a bivariate Poisson distribution by different models of univariate continuous Lindley distributions. The second is obtained by generalizing a bivariate binomial distribution with respect to its exponent when it follows any of five different univariate discrete Poisson–Lindley distributions with one or two parameters. The use of probability-generating functions is mainly employed to derive some general properties for both families and specific characteristics for each one of their members. We obtain expressions for probabilities, moments, conditional distributions, regression functions, as well as characterizations for certain bivariate models and their marginals. An attractive property of all bivariate individual models is that they contain only two or three parameters, and one of them is readily estimated by simple ratios of their sample means. This feature, and since all marginal distributions are over-dispersed, strongly suggests their potential use to describe bivariate dependent count data in many different areas.

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“…A Monte Carlo simulation study is repeated 1000 times for different sample sizes n = 50, 75, 100, 200 and parameter values (" = 1.0, F = 0.3, B = 0.5), (" = 1.0, F = 0.3, B = 0.5) and (" = 1.0, F = 0.3, B = 0.5). [6] suggested an algorithm for the simulation study employing the following steps. where I(•) is an indicator function and ³²\d8oe £ 9 is the standard error of the estimate oe -.…”

Section: Simulation Studymentioning

confidence: 99%

The Topp-Leone Weibull Distribution: Its Properties and Application

Tuoyo

Opone

Ekhosuehi

2021

EJMS

Self Cite

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This paper presents a new generalization of the Topp-Leone distribution called the Topp-Leone Weibull Distribution (TLWD). Some of the mathematical properties of the proposed distribution are derived, and the maximum likelihood estimation method is adopted in estimating the parameters of the proposed distribution. An application of the proposed distribution alongside with some well-known distributions belonging to the Topp-Leone generated family of distributions, to a real lifetime data set reveals that the proposed distribution exhibits more flexibility in modeling lifetime data based on some comparison criteria such as maximized log-likelihood, Akaike Information Criterion [AIC=2k-2 log⁡(L) ], Kolmogorov-Smirnov test statistic (K-S) and Anderson Darling test statistic (A*) and Crammer-Von Mises test statistic (W*).

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A New Generalized Two Parameter Lindley Distribution

Cited by 9 publications

References 8 publications

Exploring a New Lifetime Distribution for Modelling the Waiting Time of Bank Customers

Exploring a New Lifetime Distribution for Modelling the Waiting Time of Bank Customers

Bivariate Discrete Poisson–Lindley Distributions

The Topp-Leone Weibull Distribution: Its Properties and Application

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