On the maximal deviation of kernel regression estimators with NMAR response variables

Majid Mojirsheibani ()
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Majid Mojirsheibani: California State University

Statistical Papers, 2022, vol. 63, issue 5, No 13, 1677-1705

Abstract: Abstract This article focuses on the problem of kernel regression estimation in the presence of nonignorable incomplete data with particular focus on the limiting distribution of the maximal deviation of the proposed estimators. From an applied point of view, such a limiting distribution enables one to construct asymptotically correct uniform bands, or perform tests of hypotheses, for a regression curve when the available data set suffers from missing (not necessarily at random) response values. Furthermore, such asymptotic results have always been of theoretical interest in mathematical statistics. We also present some numerical results that further confirm and complement the theoretical developments of this paper.

Keywords: Kernel regression; Maximal deviation; Nonignorble missing; Uniform bands (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00362-022-01293-0

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