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}
}
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… Expand
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References

SHOWING 1-10 OF 81 REFERENCES
Approximate inference in generalized linear mixed models
Testing for genetic associations in arbitrarily structured populations
FaST linear mixed models for genome-wide association studies
Regression Shrinkage and Selection via the Lasso
A unified mixed-model method for association mapping that accounts for multiple levels of relatedness
A multi-marker association method for genome-wide association studies without the need for population structure correction
Stability Selection
...
1
2
3
4
5
...