Non-asymptotic approach to varying coefficient model

Olga Klopp (1) , Marianna Pensky (2)

1 MODAL'X - Modélisation aléatoire de Paris X
2 Department of Mathematics, University of Central Florida

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.

Mots clés: varying coefficient model; low rank matrix estimation; statistical learning

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Olga Klopp, Marianna Pensky. Non-asymptotic approach to varying coefficient model. Electronic Journal of Statistics , 2013, 7, pp.454 - 479. ⟨hal-00752131v2⟩