• Bernoulli
  • Volume 19, Number 3 (2013), 721-747.

Single index regression models in the presence of censoring depending on the covariates

Olivier Lopez, Valentin Patilea, and Ingrid Van Keilegom

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Consider a random vector $(X',Y)'$, where $X$ is $d$-dimensional and $Y$ is one-dimensional. We assume that $Y$ is subject to random right censoring. The aim of this paper is twofold. First, we propose a new estimator of the joint distribution of $(X',Y)'$. This estimator overcomes the common curse-of-dimensionality problem, by using a new dimension reduction technique. Second, we assume that the relation between $X$ and $Y$ is given by a mean regression single index model, and propose a new estimator of the parameters in this model. The asymptotic properties of all proposed estimators are obtained.

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Bernoulli, Volume 19, Number 3 (2013), 721-747.

First available in Project Euclid: 26 June 2013

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curse-of-dimensionality dimension reduction multivariate distribution right censoring semiparametric regression survival analysis


Lopez, Olivier; Patilea, Valentin; Van Keilegom, Ingrid. Single index regression models in the presence of censoring depending on the covariates. Bernoulli 19 (2013), no. 3, 721--747. doi:10.3150/12-BEJ464.

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