2010
DOI: 10.1007/s10260-010-0140-1
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A bivariate regression model for matched paired survival data: local influence and residual analysis

Abstract: Farlie–Gumbel–Morgenstern distribution, Bivariate failure time, Archimedean copula, Local influence, Residual analysis,

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Cited by 14 publications
(10 citation statements)
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“…In this section, we perform a statistical analysis on the renal insufficiency study originally presented by McGilchrist and Aisbett (1991) using the BKwW regression model. Recently, these data are analyzed by Barriga et al (2010) by considering a location scale model for bivariate survival times based on the proposal of a copula to model the dependence of bivariate survival data. The dataset refers to the occurrence times of two distinct infection events in patients suffering from renal insufficiency.…”
Section: Application: Renal Insufficiency Datamentioning
confidence: 99%
“…In this section, we perform a statistical analysis on the renal insufficiency study originally presented by McGilchrist and Aisbett (1991) using the BKwW regression model. Recently, these data are analyzed by Barriga et al (2010) by considering a location scale model for bivariate survival times based on the proposal of a copula to model the dependence of bivariate survival data. The dataset refers to the occurrence times of two distinct infection events in patients suffering from renal insufficiency.…”
Section: Application: Renal Insufficiency Datamentioning
confidence: 99%
“…Individual models for each event are based on independence assumptions and do not allow for inferences in a possible association. The use of bivariate models seem more adequate and this approach has being used under different approaches and can be found in Barriga et al (2010), Chatterjee and Shih (2001), Fachini et al (2014), and Núñez (2005). Besides the use of the classical multivariate parametric distributions, copulas can also be used to join marginal models into multivariate models.…”
Section: Introductionmentioning
confidence: 99%
“…and Lawless 2005; Barriga et al 2010). Typically, each marginal distribution is assumed to follow a parametric model, F X k,j (x k,j ) ≡ F j (x k,j ; θ j (z k )), j = 1, .…”
mentioning
confidence: 99%