Facultad de Ciencias Básicas

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http://repositorioabierto.uantof.cl/handle/uantof/1

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Examinando Facultad de Ciencias Básicas por Autor "Arnold, Barry C."

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  • Miniatura
    Ítem
    A Bivariate Power Lindley Survival Distribution
    (MDPI, 2024) Martínez-Flórez, Guillermo; Arnold, Barry C.; Gómez, Héctor W.
    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
  • Miniatura
    Ítem
    Univariate and bivariate models related to the generalized epsilon-skew-Cauchy distribution
    (MDPI, 2019) Arnold, Barry C.; Gómez, Héctor W.; Varela, Héctor; Vidal, Ignacio
    In this paper, we consider a stochastic representation of the epsilon–skew–Cauchy distribution, viewed as a member of the family of skewed distributions discussed in Arellano-Valle et al. (2005). The stochastic representation facilitates derivation of distributional properties of the model. In addition, we introduce symmetric and asymmetric extensions of the Cauchy distribution, together with an extension of the epsilon–skew–Cauchy distribution. Multivariate versions of these distributions can be envisioned. Bivariate examples are discussed in some detail.