Journal
JOURNAL OF APPLIED STATISTICS
Volume 39, Issue 1, Pages 151-160Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/02664763.2011.578621
Keywords
bias correction; mean-squared prediction error; random forests; regression; simulation
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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.
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