文章基本信息

标题：Non-asymptotic approach to varying coefficient model
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作者：Olga Klopp ; Marianna Pensky
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2013
卷号：7
页码：454-479
DOI：10.1214/13-EJS778
语种：English
出版社：Institute of Mathematical Statistics
摘要：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.
关键词：Varying coefficient model;low rank matrix es timation;statistical learning.