Electronic Journal of Statistics

Efficient model selection in semivarying coefficient models

Hohsuk Noh and Ingrid Van Keilegom

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Varying coefficient models are useful extensions of classical linear models. In practice, some of the coefficients may be just constant, while other coefficients are varying. Several methods have been developed to utilize the information that some coefficient functions are constant to improve estimation efficiency. However, in order for such methods to really work, the information about which coefficient functions are constant should be given in advance. In this paper, we propose a computationally efficient method to discriminate in a consistent way the constant coefficient functions from the varying ones. Additionally, we compare the performance of our proposal with that of previous methods developed for the same purpose in terms of model selection accuracy and computing time.

Article information

Electron. J. Statist., Volume 6 (2012), 2519-2534.

First available in Project Euclid: 4 January 2013

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G08: Nonparametric regression
Secondary: 62G20: Asymptotic properties

Bayesian Information Criterion boundary problem local polynomial estimator variable selection


Noh, Hohsuk; Van Keilegom, Ingrid. Efficient model selection in semivarying coefficient models. Electron. J. Statist. 6 (2012), 2519--2534. doi:10.1214/12-EJS762. https://projecteuclid.org/euclid.ejs/1357307948

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