Kernel regression estimation for incomplete data with applications

Majid Mojirsheibani () and Timothy Reese ()
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Majid Mojirsheibani: California State University
Timothy Reese: California State University

Statistical Papers, 2017, vol. 58, issue 1, No 10, 185-209

Abstract: Abstract Methods are proposed to construct kernel estimators of a regression function in the presence of incomplete data. Furthermore, exponential upper bounds are derived on the performance of the $$L_p$$ L p norms of the proposed estimators, which can then be used to establish various strong convergence results. The presence of incomplete data points are handled by a Horvitz–Thompson-type inverse weighting approach, where the unknown selection probabilities are estimated by both kernel regression and least-squares methods. As an immediate application of these results, the problem of nonparametric classification with partially observed data will be studied.

Keywords: Regular kernels; Incomplete data; Convergence; Regression; Classification; 62G08 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s00362-015-0693-z

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