Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/6671
Title: Rates of convergence of estimates, Kolmogorov's entropy and the dimensionality reduction principle in regression
Authors: Nicoleris, Theodoros 
Yatracos, Yannis G. 
Nicoleris, Theodoros 
Keywords: Convergence;Estimation
Issue Date: 1997
Publisher: Project Euclid
Source: Annals of Statistics, 1997, Volume 25, Issue 6, Pages 2493-2511
Abstract: L1-optimal minimum distance estimators are provided for a projection pursuit regression type function with smooth functional components that are either additive or multiplicative, in the presence of or without interactions. The obtained rates of convergence of the estimate to the true parameter depend on Kolmogorov's entropy of the assumed model and confirm Stone's heuristic dimensionality reduction principle. Rates of convergence are also obtained for the error in estimating the derivatives of a regression type function.
URI: http://ktisis.cut.ac.cy/handle/10488/6671
ISSN: 00905364
DOI: 10.1214/aos/1030741082
Type: Article
Appears in Collections:Άρθρα/Articles

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