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• W1967787804 abstract "Let (Xi , Yi ) (i = 1,..., n) be n replications of a random vector (X, Y ), where Y is supposed to be subject to random right censoring. The data (Xi , Yi ) are assumed to come from a stationary α-mixing process. We consider the problem of estimating the function m(x) = E(φ(Y )|X = x), for some known transformation φ. Particular choices of φ lead to the conditional moment function of Y given X , or the conditional distribution of Y given X . This problem is approached in the following way : first, we introduce a transformed variable Y ∗ i , that is not subject to censoring and satisfies the relation E(φ(Yi |Xi = x) = E(Y ∗ i |Xi = x), and then we estimate m(x) by applying local linear regression techniques to the pseudo-data (Xi , ˆ Y ∗ i ), where ˆ Y ∗ i is a certain estimator of Y ∗ i . The asymptotic properties of the proposed estimator are established. We investigate the performance of the estimator for small samples through a simulation study, and we discuss the optimal choice of the transformation Y ∗ i . As a by-product, we obtain a general result on the uniform rate of convergence of kernel type estimators of functionals of an unknown distribution function, under strong mixing assumptions. This result is of independent interest, and can be applied in a wide variety of contexts." @default.
• W1967787804 created "2016-06-24" @default.
• W1967787804 creator A5025169649 @default.
• W1967787804 creator A5081152853 @default.
• W1967787804 date "2008-06-01" @default.
• W1967787804 modified "2023-10-14" @default.
• W1967787804 title "Non-parametric Regression with Dependent Censored Data" @default.
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• W1967787804 doi "https://doi.org/10.1111/j.1467-9469.2007.00586.x" @default.
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