Integrative sparse partial least squares

@article{Liang2021IntegrativeSP,
  title={Integrative sparse partial least squares},
  author={Weijuan Liang and Shuangge Ma and Qingzhao Zhang and Tingyu Zhu},
  journal={Statistics in Medicine},
  year={2021},
  volume={40},
  pages={2239 - 2256}
}
Partial least squares, as a dimension reduction technique, has become increasingly important for its ability to deal with problems with a large number of variables. Since noisy variables may weaken estimation performance, the sparse partial least squares (SPLS) technique has been proposed to identify important variables and generate more interpretable results. However, the small sample size of a single dataset limits the performance of conventional methods. An effective solution comes from… 

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