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Nonparametric estimation of the log odds ratio for sparse data by kernel smoothing

Author

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  • Chen, Ziqi
  • Shi, Ning-Zhong
  • Gao, Wei

Abstract

Regression analysis of the odds ratios for sparse data has received a lot of attention. However, existing works are restricted to the parametric case, and a parametric model may be a misspecification, which may lead to biased and inefficient estimators. Little attention is received for nonparametric regression analysis of the odds ratios. Based on kernel smoothing techniques, we propose two simple estimators of the log odds-ratio function for sparse data. Large sample properties of the estimators are derived, and the methods proposed are evaluated through simulation.

Suggested Citation

  • Chen, Ziqi & Shi, Ning-Zhong & Gao, Wei, 2011. "Nonparametric estimation of the log odds ratio for sparse data by kernel smoothing," Statistics & Probability Letters, Elsevier, vol. 81(12), pages 1802-1807.
    • Handle: RePEc:eee:stapro:v:81:y:2011:i:12:p:1802-1807
    DOI: 10.1016/j.spl.2011.06.017
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    References listed on IDEAS

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    1. Hua Yun Chen, 2007. "A Semiparametric Odds Ratio Model for Measuring Association," Biometrics, The International Biometric Society, vol. 63(2), pages 413-421, June.
    2. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643, October.
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    Cited by:

    1. Sung Jae Jun & Sokbae Lee, 2022. "Average Adjusted Association: Efficient Estimation with High Dimensional Confounders," Papers 2205.14048, arXiv.org, revised Apr 2023.

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