Corpus ID: 88516563

Spectral Deconfounding via Perturbed Sparse Linear Models

@article{Cevid2018SpectralDV,
  title={Spectral Deconfounding via Perturbed Sparse Linear Models},
  author={Domagoj Cevid and Peter Buhlmann and N. Meinshausen},
  journal={arXiv: Methodology},
  year={2018}
}
  • Domagoj Cevid, Peter Buhlmann, N. Meinshausen
  • Published 2018
  • Mathematics
  • arXiv: Methodology
  • Standard high-dimensional regression methods assume that the underlying coefficient vector is sparse. This might not be true in some cases, in particular in presence of hidden, confounding variables. Such hidden confounding can be represented as a high-dimensional linear model where the sparse coefficient vector is perturbed. For this model, we develop and investigate a class of methods that are based on running the Lasso on preprocessed data. The preprocessing step consists of applying certain… CONTINUE READING
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