A kernel-type regression estimator for NMAR response variables with applications to classification

Majid Mojirsheibani and Arin Khudaverdyan

Statistics & Probability Letters, 2024, vol. 215, issue C

Abstract: This work deals with the problem of nonparametric estimation of a regression function when the response variable may be missing according to a not-missing-at-random (NMAR) setup. To assess the theoretical performance of our estimators, we study their strong convergence properties in Lp norms where we also look into their rates of convergence. We also study applications of our results to the problem of statistical classification in semi-supervised learning.

Keywords: Regression; Missing data; Kernel; Convergence; Margin (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.spl.2024.110246

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