文章基本信息

标题：An improved global risk bound in concave regression
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作者：Sabyasachi Chatterjee
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2016
卷号：10
期号：1
页码：1608-1629
DOI：10.1214/16-EJS1151
语种：English
出版社：Institute of Mathematical Statistics
摘要：A new risk bound is presented for the problem of convex/concave function estimation, using the least squares estimator. The best known risk bound, as had appeared in Guntuboyina and Sen, scaled like $\log(en)\:n^{-4/5}$ under the mean squared error loss, up to a constant factor. The authors in [8] had conjectured that the logarithmic term may be an artifact of their proof. We show that indeed the logarithmic term is unnecessary and prove a risk bound which scales like $n^{-4/5}$ up to constant factors. Our proof technique has one extra peeling step than in a usual chaining type argument. Our risk bound holds in expectation as well as with high probability and also extends to the case of model misspecification, where the true function may not be concave.