Variable Selection for Partially Linear Models with Measurement Errors.

  title={Variable Selection for Partially Linear Models with Measurement Errors.},
  author={Hua Liang and Runze Li},
  journal={Journal of the American Statistical Association},
  volume={104 485},
This article focuses on variable selection for partially linear models when the covariates are measured with additive errors. We propose two classes of variable selection procedures, penalized least squares and penalized quantile regression, using the nonconvex penalized principle. The first procedure corrects the bias in the loss function caused by the measurement error by applying the so-called correction-for-attenuation approach, whereas the second procedure corrects the bias by using… CONTINUE READING
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