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

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  • Mojirsheibani, Majid
  • Khudaverdyan, Arin

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.

Suggested Citation

  • Mojirsheibani, Majid & Khudaverdyan, Arin, 2024. "A kernel-type regression estimator for NMAR response variables with applications to classification," Statistics & Probability Letters, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:stapro:v:215:y:2024:i:c:s0167715224002153
    DOI: 10.1016/j.spl.2024.110246
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    References listed on IDEAS

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