Coefficient‐Based Regression with Non‐Identical Unbounded Sampling

Jia Cai

Abstract and Applied Analysis, 2013, vol. 2013, issue 1

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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https://doi.org/10.1155/2013/134727

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jnlaaa:v:2013:y:2013:i:1:n:134727

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