Error Variance Estimation in Ultrahigh-Dimensional Additive Models.

@article{Chen2018ErrorVE,
  title={Error Variance Estimation in Ultrahigh-Dimensional Additive Models.},
  author={Zhao Q. Chen and Jianqing Fan and Runze Li},
  journal={Journal of the American Statistical Association},
  year={2018},
  volume={113 521},
  pages={
          315-327
        }
}
Error variance estimation plays an important role in statistical inference for high dimensional regression models. This paper concerns with error variance estimation in high dimensional sparse additive model. We study the asymptotic behavior of the traditional mean squared errors, the naive estimate of error variance, and show that it may significantly underestimate the error variance due to spurious correlations which are even higher in nonparametric models than linear models. We further… CONTINUE READING

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SHOWING 1-10 OF 23 REFERENCES

Variance estimation using refitted cross-validation in ultrahigh dimensional regression.

  • Journal of the Royal Statistical Society. Series B, Statistical methodology
  • 2010
VIEW 6 EXCERPTS

Sparse Additive Models

C. J. Stone
  • Journal of the Royal Statistical Society , Series B
  • 1985
VIEW 14 EXCERPTS
HIGHLY INFLUENTIAL

Feature Screening via Distance Correlation Learning.

  • Journal of the American Statistical Association
  • 2012
VIEW 17 EXCERPTS

VARIABLE SELECTION IN NONPARAMETRIC ADDITIVE MODELS.

  • Annals of statistics
  • 2010
VIEW 17 EXCERPTS
HIGHLY INFLUENTIAL

ConsistentVariable Selection inAdditiveModels

L. Xue
  • Journal of the American Statistical Association
  • 2009
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Additive Regression andOtherNonparametricModels

A. W. Van Der Vaart, J. A. Wellner
  • The Annals of Statistics
  • 1996
VIEW 1 EXCERPT
HIGHLY INFLUENTIAL