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- W1583066247 abstract "This chapter deals with multivariate right-censored survival data, and a bivariate framework. The non-parametric estimator proposed by Lopez and Saint-Pierre allows us to obtain a proper joint distribution. Moreover, it presents some desirable properties: classical empirical estimator is obtained in absence of censoring, estimates of marginal distribution lead to Kaplan–Meier estimates and it is equivariant under reversal of coordinates. Estimation of several dependence measures as Kendall's coefficient is discussed. A bootstrap procedure for multivariate survival data is derived. The chapter also discusses a regression model where the response and the covariate are both randomly right-censored. The chapter introduces the bivariate distribution estimator to estimate the quantities. It provides an asymptotic independent and indentically distributed (i.i.d.) representation for the estimators. A section focuses on the estimation of dependence measures, a bootstrap procedure and regression modeling. Finally, the chapter illustrates the applications of these methods." @default.
- W1583066247 created "2016-06-24" @default.
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- W1583066247 date "2013-12-02" @default.
- W1583066247 modified "2023-10-10" @default.
- W1583066247 title "Non-Parametric Estimation of Linear Functionals of a Multivariate Distribution Under Multivariate Censoring with Applications" @default.
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- W1583066247 doi "https://doi.org/10.1002/9781118826805.ch16" @default.
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