Generalized orthogonal components regression for high dimensional generalized linear models

@article{Lin2015GeneralizedOC,
  title={Generalized orthogonal components regression for high dimensional generalized linear models},
  author={Yanzhu Lin and Min Zhang and Dabao Zhang},
  journal={Computational Statistics & Data Analysis},
  year={2015},
  volume={88},
  pages={119-127}
}
Here we propose an algorithm, named generalized orthogonal components regression (GOCRE), to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together build up a generalized linear model (GLM). This algorithm can be considered as an extension of the partial least squares (PLS) for GLMs, but overcomes several issues of existing extensions… CONTINUE READING

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