A bivariate regression model for matched paired survival data: local influence and residual analysis

Barriga, Gladys Dorotea Cacsire; Louzada, Francisco; Ortega, Edwin M. M.; Cancho, Vicente G.

doi:10.1007/s10260-010-0140-1

Cited by 14 publications

(10 citation statements)

References 31 publications

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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%

The Bivariate Kumaraswamy Weibull regression model: a complete classical and Bayesian analysis

Fachini-Gomes

Ortega

Cordeiro

et al. 2018

CSAM

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Bivariate distributions play a fundamental role in survival and reliability studies. We consider a regression model for bivariate survival times under right-censored based on the bivariate Kumaraswamy Weibull (Cordeiro et al., Journal of the Franklin Institute, 347, 1399-1429, 2010) distribution to model the dependence of bivariate survival data. We describe some structural properties of the marginal distributions. The method of maximum likelihood and a Bayesian procedure are adopted to estimate the model parameters. We use diagnostic measures based on the local influence and Bayesian case influence diagnostics to detect influential observations in the new model. We also show that the estimates in the bivariate Kumaraswamy Weibull regression model are robust to deal with the presence of outliers in the data. In addition, we use some measures of goodness-of-fit to evaluate the bivariate Kumaraswamy Weibull regression model. The methodology is illustrated by means of a real lifetime data set for kidney patients.

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Section: Application: Renal Insufficiency Datamentioning

confidence: 99%

The Bivariate Kumaraswamy Weibull regression model: a complete classical and Bayesian analysis

Fachini-Gomes

Ortega

Cordeiro

et al. 2018

CSAM

Self Cite

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“…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%

Bivariate odd-log-logistic-Weibull regression model for oral health-related quality of life

Cruz¹,

Ortega²,

Cordeiro³

et al. 2017

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We study a bivariate response regression model with arbitrary marginal distributions and joint distributions using Frank and Clayton's families of copulas. The proposed model is used for fitting dependent bivariate data with explanatory variables using the log-odd log-logistic Weibull distribution. We consider likelihood inferential procedures based on constrained parameters. For different parameter settings and sample sizes, various simulation studies are performed and compared to the performance of the bivariate odd-log-logistic-Weibull regression model. Sensitivity analysis methods (such as local and total influence) are investigated under three perturbation schemes. The methodology is illustrated in a study to assess changes on schoolchildren's oral health-related quality of life (OHRQoL) in a follow-up exam after three years and to evaluate the impact of caries incidence on the OHRQoL of adolescents.

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“…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, .…”

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RafterNet: Probabilistic predictions in multi-response regression

Hofert¹,

Prasad²,

Zhu³

2021

Preprint

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A fully nonparametric approach for making probabilistic predictions in multi-response regression problems is introduced. Random forests are used as marginal models for each response variable and, as novel contribution of the present work, the dependence between the multiple response variables is modeled by a generative neural network. This combined modeling approach of random forests, corresponding empirical marginal residual distributions and a generative neural network is referred to as RafterNet. Multiple datasets serve as examples to demonstrate the flexibility of the approach and its impact for making probabilistic forecasts.

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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,

Cited by 14 publications

References 31 publications

The Bivariate Kumaraswamy Weibull regression model: a complete classical and Bayesian analysis

The Bivariate Kumaraswamy Weibull regression model: a complete classical and Bayesian analysis

Bivariate odd-log-logistic-Weibull regression model for oral health-related quality of life

RafterNet: Probabilistic predictions in multi-response regression

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