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Non parametric regression models with additive distortions

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  • Yujie Gai
  • Jun Zhang
  • Yiping Yang

Abstract

In this article, we study the non parametric estimation of some regression curves when the data are observed with additive distortions, and these distortions for unobservable response variables and covariates are connected with a common observed confounding variable. We study the estimates of the non parametric mean function and its first derivative, the variance function, the Sharpe ratio function, and the correlation curve function. We obtain asymptotic normality results for the proposed non parametric estimators. Monte Carlo simulation experiments are conducted to examine the performance of the proposed estimators. The proposed estimators are applied to analyze a QSAR fish toxicity dataset for illustration.

Suggested Citation

  • Yujie Gai & Jun Zhang & Yiping Yang, 2024. "Non parametric regression models with additive distortions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 53(24), pages 8592-8613, December.
    • Handle: RePEc:taf:lstaxx:v:53:y:2024:i:24:p:8592-8613
    DOI: 10.1080/03610926.2023.2281894
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