Sparse probit linear mixed model

@article{Mandt2017SparsePL,
  title={Sparse probit linear mixed model},
  author={S. Mandt and F. Wenzel and Shinichi Nakajima and J. Cunningham and C. Lippert and M. Kloft},
  journal={Machine Learning},
  year={2017},
  volume={106},
  pages={1621-1642}
}
  • S. Mandt, F. Wenzel, +3 authors M. Kloft
  • Published 2017
  • Computer Science, Mathematics
  • Machine Learning
  • Linear mixed models (LMMs) are important tools in statistical genetics. When used for feature selection, they allow to find a sparse set of genetic traits that best predict a continuous phenotype of interest, while simultaneously correcting for various confounding factors such as age, ethnicity and population structure. Formulated as models for linear regression, LMMs have been restricted to continuous phenotypes. We introduce the sparse probit linear mixed model (Probit-LMM), where we… CONTINUE READING

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    References

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    SHOWING 1-10 OF 84 REFERENCES

    Regression Shrinkage and Selection via the Lasso

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL