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Bias-corrected random forests in regression

Author

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  • Guoyi Zhang
  • Yan Lu

Abstract

It is well known that random forests reduce the variance of the regression predictors compared to a single tree, while leaving the bias unchanged. In many situations, the dominating component in the risk turns out to be the squared bias, which leads to the necessity of bias correction. In this paper, random forests are used to estimate the regression function. Five different methods for estimating bias are proposed and discussed. Simulated and real data are used to study the performance of these methods. Our proposed methods are significantly effective in reducing bias in regression context.

Suggested Citation

  • Guoyi Zhang & Yan Lu, 2012. "Bias-corrected random forests in regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(1), pages 151-160, March.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:151-160
    DOI: 10.1080/02664763.2011.578621
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    References listed on IDEAS

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    1. Biau, Gérard & Devroye, Luc, 2010. "On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2499-2518, November.
    2. Lin, Yi & Jeon, Yongho, 2006. "Random Forests and Adaptive Nearest Neighbors," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 578-590, June.
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