New class of Lindley distributions: properties and applications

Hamed, Duha; Alzaghal, Ahmad

doi:10.1186/s40488-021-00127-y

Cited by 9 publications

(4 citation statements)

References 27 publications

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“…Applying new generalizations for continuous distributions has now become more attractive because this methodology can improve the goodness of fit and determine tail properties. Generalizing distributions mainly depends on adding more flexibility to known distributions by implanting a primary distribution into a more capable structure [22,23]. These features have been established by the results of many generalizations such as the beta family [24] and transformed transformer [25].…”

Section: Of 13mentioning

confidence: 99%

A New Extension of Generalized Extreme Value Distribution: Extreme Value Analysis and Return Level Estimation of the Rainfall Data

2022

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This paper presents an extension of the generalized extreme value (GEV) distribution, based on the T-X family of distributions: Gompertz-generated family of distributions that make the existing distribution more flexible called the Gompertz-general extreme value (Go-GEV) distribution. Some properties of the proposed distribution are introduced, and a new distribution is applied to actual data, namely rainfall in Lopburi Province, by comparing the proposed model with the traditional GEV distribution and estimating the return levels of the rainfall in Lopburi Province. Results showed that the Go-GEV was an alternative flexible distribution for extreme values that fitted with actual data and described the maximum rainfall better than the traditional GEV distribution. The probability density functions of the Go-GEV distribution had various shapes including left-skewed, right-skewed and close to symmetric. Estimation of the return levels of rainfall values in Lopburi Province by the Go-GEV distribution indicated that Buachum Station should be monitored because it had higher precipitation and return levels than Lopburi Station at 2, 5, 10 and 15 years. HIGHLIGHTS The analysis of data with extreme values is complex but ignoring an observation just because it is unacceptable is not best practice. One of the widely used tools in such situations is “Extreme Value Theory” The Gompertz-generalized extreme value (Go-GEV) distribution is a new extension of GEV distribution using deffinition of Gompertz-generalized family of distributions The developed Go-GEV model described the maximum rainfall data better than the GEV distribution because the new extension distribution was more flexible and its probability density function had a more diverse shape, such as both left-skewed, right-skewed and close to symmetrical The developed Go-GEV model is a flexible alternative for applying the model to extreme value data GRAPHICAL ABSTRACT

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Section: Of 13mentioning

confidence: 99%

A New Extension of Generalized Extreme Value Distribution: Extreme Value Analysis and Return Level Estimation of the Rainfall Data

2022

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“…Hamed D., and Alzaaghal A. [8] introduced new class of Lindley distributions because of having only one parameter, the Lindley distribution does not provide enough flexibility for analyzing different types of lifetime data. To increase the flexibility for modeling purposes, it will be useful to consider further generalizations of this distribution.…”

Section: Introductionmentioning

confidence: 99%

The Doubly Truncated Generalized Log-Lindley Distribution

Teamah,

Elmenify

2023

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The aim of this paper is to present generalized log-Lindely (GLL) distribution, as a new model, and find doubly truncated generalized log-Lindely (DTGLL) distribution, truncation in probability distributions may occur in many studies such as life testing, and reliability. We illustrate the applicability of GLL and DTGLL distributions by Real data application. The GLL distribution can handle the risk rate functions in the form of panich and increase. This property makes GLL useful in survival analysis. Various statistical and reliability measures are obtained for the model, including hazard rate function, moments, moment generating function, mean and variance, quantiles function, Skewness and kurtosis, mean deviations, mean inactivity time and strong mean inactivity time. The estimation of model parameters is justified by the maximum Likelihood method. An application to real data shows that DTGLL distribution can provide better suitability than GLL and some other known distributions.

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“…Ghitany et al [8] studied statistical properties of the Lindley distribution. ey proved the applicability of the distribution for the waiting times before service of the bank customers and its superiority in comparison with the exponential distribution, see also [9][10][11][12][13][14][15][16][17][18][19][20][21]. Shankar and Mishra [22] developed a two-parameter version of the one-parameter Lindley distribution.…”

Section: Introductionmentioning

confidence: 99%

On Statistical Properties of a New Bivariate Modified Lindley Distribution with an Application to Financial Data

Elhassanein

2022

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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.

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New class of Lindley distributions: properties and applications

Cited by 9 publications

References 27 publications

A New Extension of Generalized Extreme Value Distribution: Extreme Value Analysis and Return Level Estimation of the Rainfall Data

A New Extension of Generalized Extreme Value Distribution: Extreme Value Analysis and Return Level Estimation of the Rainfall Data

The Doubly Truncated Generalized Log-Lindley Distribution

On Statistical Properties of a New Bivariate Modified Lindley Distribution with an Application to Financial Data

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