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2013 Coefficient-Based Regression with Non-Identical Unbounded Sampling
Jia Cai
Abstr. Appl. Anal. 2013(SI32): 1-8 (2013). DOI: 10.1155/2013/134727


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.


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Jia Cai. "Coefficient-Based Regression with Non-Identical Unbounded Sampling." Abstr. Appl. Anal. 2013 (SI32) 1 - 8, 2013.


Published: 2013
First available in Project Euclid: 26 February 2014

zbMATH: 1302.62089
MathSciNet: MR3064407
Digital Object Identifier: 10.1155/2013/134727

Rights: Copyright © 2013 Hindawi

Vol.2013 • No. SI32 • 2013
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