Authors

O. Klopp;M. Pensky

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Abbreviated Journal Title

Electron. J. Stat.

Keywords

Varying coefficient model; low rank matrix estimation; statistical; learning; LONGITUDINAL DATA; MATRIX COMPLETION; SPLINE ESTIMATION; INFERENCE; Statistics & Probability

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.

Journal Title

Electronic Journal of Statistics

Volume

7

Publication Date

1-1-2013

Document Type

Article

Language

English

First Page

454

Last Page

479

WOS Identifier

WOS:000321054000001

ISSN

1935-7524

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