Coefficient-based regression with non-identical unbounded sampling
type: Item
instance of: scholarly article
title: Coefficient-based regression with non-identical unbounded sampling
zbMATH Open document ID: 1302.62089
DOI: 10.1155/2013/134727
author: Jia Cai
published in: Abstract and Applied Analysis
publication date: 2014-06-20T00:00:00Z
review text: Summary: We investigate a coefficient-based least squares regression problem with indefinite kernels from non-identical unbounded sampling processes. Here non-identical unbounded sampling means the samples are drawn independently but not identically from unbounded sampling processes. The kernel is not necessarily symmetric or positive semi-definite. This leads to additional difficulty in the error analysis. By introducing a suitable reproducing kernel Hilbert space (RKHS) and a suitable intermediate integral operator, elaborate analysis is presented by means of a novel technique for the sample error. This leads to satisfactory results.
Mathematics Subject Classification ID: 62G08
zbMATH DE Number: 6306047
Wikidata QID: Q58915332
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2013/134727
OpenAlex ID: W2007486044
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description: scientific article; zbMATH DE number 6306047