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Comments on: Nonparametric inference with generalized likelihood ratio tests

Nonparametric sparsity

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Abstract

We discuss the issues raised by Fan and Jiang in the context of high dimensional models and argue that fitting sparse nonparametric models might be preferable to hypothesis testing.

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References

  • Ravikumar P, Lafferty J, Liu H, Wasserman L (2007) SpAM: sparse additive models. Adv Neural Inf Process Syst (NIPS) 21 (to appear)

  • Wasserman L, Roeder K (2007) Multistage variable selection: screen and clean. ArXiv: 0704.1139

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Authors and Affiliations

  1. Computer Science Department and Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA

    John Lafferty

  2. Statistics Department and Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA

    Larry Wasserman

Authors
  1. John Lafferty
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  2. Larry Wasserman
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Correspondence to John Lafferty.

Additional information

This comment refers to the invited paper available at: http://dx.doi.org/10.1007/s11749-007-0080-8.

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Lafferty, J., Wasserman, L. Comments on: Nonparametric inference with generalized likelihood ratio tests. TEST 16, 453–455 (2007). https://doi.org/10.1007/s11749-007-0084-4

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  • DOI: https://doi.org/10.1007/s11749-007-0084-4

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