Non‐parametric Regression with Dependent Censored Data

Anouar El Ghouch and Ingrid Van Keilegom (ingrid.vankeilegom@kuleuven.be)

Scandinavian Journal of Statistics, 2008, vol. 35, issue 2, 228-247

Abstract: 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. This problem is approached in the following way: first, we introduce a transformed variable , that is not subject to censoring and satisfies the relation , and then we estimate m(x) by applying local linear regression techniques. 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.

Date: 2008
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Citations: View citations in EconPapers (9)

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https://doi.org/10.1111/j.1467-9469.2007.00586.x

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Persistent link: https://EconPapers.repec.org/RePEc:bla:scjsta:v:35:y:2008:i:2:p:228-247

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