Influence diagnostics in log-Birnbaum–Saunders regression models with censored data

Leiva, Víctor; Barros, Michelli; Paula, Gilberto A.; Galea, Manuel

doi:10.1016/j.csda.2006.09.020

Cited by 130 publications

(108 citation statements)

References 25 publications

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“…For illustration purposes, we consider the uncensored part of a data set analysed by Leiva et al (2007) corresponding to the survival times (T , in months) of 48 patients who were treated with alkylating agents for multiple myeloma. These data (which we will henceforth call myeloma) are: 1, 1, 2, 2, 2, 3,5,5,6,6,6,6,7,7,7,9,11,11,11,11,11,13,14,15,16,16,17,17,18,19,19,24,25,26,32,35,37,41,42,51,52,54,58,66,67,88,89,92.…”

Section: An Application To Biometrymentioning

confidence: 99%

The extreme value Birnbaum-Saunders model, its moments and an application in biometry

Gomes

Феррейра

Leiva

2012

Biometrical Letters

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SummaryThe Birnbaum-Saunders (BS) model is a life distribution that has been widely studied and applied. Recently, a new version of the BS distribution based on extreme value theory has been introduced, named the extreme value Birnbaum-Saunders (EVBS) distribution. In this article we provide some further details on the EVBS models that can be useful as a supplement to the existing results. We use these models to analyse real survival time data for patients treated with alkylating agents for multiple myeloma. This analysis allow us to show the adequacy of these new statistical distributions and identify them as models useful for medical practitioners in order to predict survival times for such patients and evaluate changes in their treatment dose.

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Section: An Application To Biometrymentioning

confidence: 99%

The extreme value Birnbaum-Saunders model, its moments and an application in biometry

Gomes

Феррейра

Leiva

2012

Biometrical Letters

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“…The BS distribution is a popular model, commonly used in different areas of sciences and engineering; for example, Desmond (1985) considered the BS distribution as a model in biology, Leiva et al (2007), Barros et al (2008) presented some applications in the medical field, and Podlaski (2008), Leiva et al (2010) and Vilca et al (2010) used it to model data related to the forestry and environmental sciences.…”

Section: Introductionmentioning

confidence: 99%

An Extension of the Birnbaum-Saunders Distribution Based on Skew-Normal t Distribution

Jamalizadeh¹,

Amirzadeh²,

Hashemi³

2015

JSRI

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Abstract. In this paper, we introducte a family of univariate BirnbaumSaunders distributions arising from the skew-normal-t distribution. We obtain several properties of this distribution such as its moments, the maximum likelihood estimation procedure via an EM-algorithm and a method to evaluate standard errors using the EM-algorithm. Finally, we apply these methods to a real data set to demonstrate its flexibility and conduct a simulation study to demonstrate the usefulness of this distribution when compared to the ordinary Birnbaum-Saunders and skew-normal Birnbaum-Saunders distributions.

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“…Later, Rieck [12] defined that, if δ = 2/α and g(Y; γ, σ) = sinh(y − γ/σ) in (1) then Y follow a four parameters sinh-normal (SHN) distribution with shape parameters ν ∈ ℝ , α > 0, location parameter γ ∈ ℝ, and scale parameter σ > 0, and the notation Y~SHN(α, γ, σ, ν) is used, which is reduced simply to Y~SHN(α, γ, σ) when ν = 0; for more details and applications of the SHN distribution see Rieck and Nedelman [13], Galea et al [4], and Leiva et al [9]. If Y~SHN(α, γ, 2), then T = exp(Y) follows the Birnbaum-Saunders (BS) distribution with shape parameter α > 0 and scale parameter β = exp(γ) > 0 , which is denoted by T~BS(α, β); see Birnbaum and Saunders [3], Johnson et al [8], and Sanhueza et al [14].…”

Section: Introductionmentioning

confidence: 99%

On the skewed sinh-normal distribution

Ashour¹,

Saad

2015

IJASP

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Leiva et al. [10] introduce the skewed sinh-normal distribution, which is a skewed version of the sinh-normal distribution, discussed some of its properties and characterized an extension of the Birnbaum-Saunders distribution associated with this distribution. In this paper, we will introduce further properties of the skewed sinh-normal distribution, and introduce a new approximate form of its probability density function and cumulative distribution function (cdf), along with numerical comparison between the exact and approximate values of its cdf. Moreover, a random number generator for the distribution will be suggested and a goodness of fit test will be carried out to examine the effectiveness of the proposed random number generator. Finally, maximum likelihood estimation for the unknown parameter of the skewed sinh-normal distribution will be investigated, and numerical illustration for the new results will be discussed.

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Influence diagnostics in log-Birnbaum–Saunders regression models with censored data

Cited by 130 publications

References 25 publications

The extreme value Birnbaum-Saunders model, its moments and an application in biometry

The extreme value Birnbaum-Saunders model, its moments and an application in biometry

An Extension of the Birnbaum-Saunders Distribution Based on Skew-Normal t Distribution

On the skewed sinh-normal distribution

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