Coefficient-Based Regression with Non-Identical Unbounded Sampling

Jia Cai

Abstract and Applied Analysis, 2013, vol. 2013, 1-8

Abstract:

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.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlaaa:134727

DOI: 10.1155/2013/134727

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