A Bivariate Power Lindley Survival Distribution

We introduce and investigate the properties of new families of univariate and bivariate distributions based on the survival function of the Lindley distribution. The univariate distribution, to reflect the nature of its construction, is called a power Lindley survival distribution. The basic distributional properties of this model are described. Maximum likelihood estimates of the parameters of the distribution are studied and the corresponding information matrix is identified. A bivariate power Lindley survival distribution is introduced using the technique of conditional specification. The corresponding joint density and marginal and conditional densities are derived. The product moments of the distribution are obtained, together with bounds on the range of correlations that can be exhibited by the model. Estimation of the parameters of the model is implemented by maximizing the corresponding pseudo-likelihood function. The asymptotic variance–covariance matrix of these estimates is investigated. A simulation study is performed to illustrate the performance of these parameter estimates. Finally some examples of model fitting using real-world data sets are described.

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Publication Details

Journal
Mathematics
Published
2024-10-24
DOI
https://doi.org/10.3390/math12213334
Primary Topic
Genetics and Plant Breeding
Type
article
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article

A Bivariate Power Lindley Survival Distribution

Héctor W. Gómez, Barry C. Arnold, Guillermo Martínez‐Flórez
Mathematics
Genetics and Plant Breeding
article

A Bivariate Power Lindley Survival Distribution

Héctor W. Gómez, Barry C. Arnold, Guillermo Martínez‐Flórez
article en

Abstract

We introduce and investigate the properties of new families of univariate and bivariate distributions based on the survival function of the Lindley distribution. The univariate distribution, to reflect the nature of its construction, is called a power Lindley survival distribution. The basic distributional properties of this model are described. Maximum likelihood estimates of the parameters of the distribution are studied and the corresponding information matrix is identified. A bivariate power Lindley survival distribution is introduced using the technique of conditional specification. The corresponding joint density and marginal and conditional densities are derived. The product moments of the distribution are obtained, together with bounds on the range of correlations that can be exhibited by the model. Estimation of the parameters of the model is implemented by maximizing the corresponding pseudo-likelihood function. The asymptotic variance–covariance matrix of these estimates is investigated. A simulation study is performed to illustrate the performance of these parameter estimates. Finally some examples of model fitting using real-world data sets are described.

MathematicsVol. 12(21)
University of California, Riverside (US), Universidad de Antofagasta (CL), University of Córdoba (CO)
Openalex Percentile: Top 90%
Genetics and Plant Breeding
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