Electronic Journal of Statistics

Non-asymptotic approach to varying coefficient model

Olga Klopp and Marianna Pensky

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Abstract

In the present paper we consider the varying coefficient model which represents a useful tool for exploring dynamic patterns in many applications. Existing methods typically provide asymptotic evaluation of precision of estimation procedures under the assumption that the number of observations tends to infinity. In practical applications, however, only a finite number of measurements are available. In the present paper we focus on a non-asymptotic approach to the problem. We propose a novel estimation procedure which is based on recent developments in matrix estimation. In particular, for our estimator, we obtain upper bounds for the mean squared and the pointwise estimation errors. The obtained oracle inequalities are non-asymptotic and hold for finite sample size.

Article information

Source
Electron. J. Statist., Volume 7 (2013), 454-479.

Dates
First available in Project Euclid: 13 February 2013

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1360764852

Digital Object Identifier
doi:10.1214/13-EJS778

Mathematical Reviews number (MathSciNet)
MR3020429

Zentralblatt MATH identifier
1337.62077

Subjects
Primary: 62J99: None of the above, but in this section 62H12: Estimation
Secondary: 60G57: Random measures

Keywords
Varying coefficient model low rank matrix estimation statistical learning

Citation

Klopp, Olga; Pensky, Marianna. Non-asymptotic approach to varying coefficient model. Electron. J. Statist. 7 (2013), 454--479. doi:10.1214/13-EJS778. https://projecteuclid.org/euclid.ejs/1360764852


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