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

Full-text: Open access

Abstract

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

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

Dates
First available in Project Euclid: 19 March 2014

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1395234511

Digital Object Identifier
doi:10.1214/14-EJS882

Mathematical Reviews number (MathSciNet)
MR3189554

Zentralblatt MATH identifier
1282.62108

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

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

Citation

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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