Electronic Journal of Statistics

Varying coefficient models having different smoothing variables with randomly censored data

Seong J. Yang, Anouar El Ghouch, and Ingrid Van Keilegom

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The varying coefficient model is a useful alternative to the classical linear model, since the former model is much richer and more flexible than the latter. We propose estimators of the coefficient functions for the varying coefficient model in the case where different coefficient functions depend on different covariates and the response is subject to random right censoring. Since our model has an additive structure and requires multivariate smoothing we employ a smooth backfitting technique, that is known to be an effective way to avoid “the curse of dimensionality” in structured nonparametric models. The estimators are based on synthetic data obtained by an unbiased transformation. The asymptotic normality of the estimators is established, a simulation study illustrates the reliability of our estimators, and the estimation procedure is applied to data on drug abuse.

Article information

Electron. J. Statist., Volume 8, Number 1 (2014), 226-252.

First available in Project Euclid: 19 March 2014

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G08: Nonparametric regression
Secondary: 62N01: Censored data models

Smooth backfitting unbiased transformation random right censoring local polynomial smoothing bandwidth parameter curse of dimensionality


Yang, Seong J.; El Ghouch, Anouar; Van Keilegom, Ingrid. Varying coefficient models having different smoothing variables with randomly censored data. Electron. J. Statist. 8 (2014), no. 1, 226--252. doi:10.1214/14-EJS882. https://projecteuclid.org/euclid.ejs/1395234511

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