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Failure time regression with continuous covariates measured with error
Author(s) -
Zhou Halbo,
Wang C.Y.
Publication year - 2000
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/1467-9868.00255
Subject(s) - covariate , estimator , statistics , nonparametric statistics , inference , kernel (algebra) , kernel regression , mathematics , nonparametric regression , regression , regression analysis , kernel smoother , computer science , econometrics , kernel method , artificial intelligence , combinatorics , radial basis function kernel , support vector machine
We consider failure time regression analysis with an auxiliary variable in the presence of a validation sample. We extend the nonparametric inference procedure of Zhou and Pepe to handle a continuous auxiliary or proxy covariate. We estimate the induced relative risk function with a kernel smoother and allow the selection probability of the validation set to depend on the observed covariates. We present some asymptotic properties for the kernel estimator and provide some simulation results. The method proposed is illustrated with a data set from an on‐going epidemiologic study.