A comprehensive tutorial is given in: An overview of the implementation is given in: The theory and the package (until version 2.0) are described in: Details of stability selection in the context of boosting are described in:

Hofner B, Mayr A, Robinzonov N, Schmid M (2014). “Model-based Boosting in R: A Hands-on Tutorial Using the R Package mboost.” Computational Statistics, 29, 3–35.

Hothorn T, Buehlmann P, Kneib T, Schmid M, Hofner B (2010). “Model-based Boosting 2.0.” Journal of Machine Learning Research, 11, 2109–2113.

Buehlmann P, Hothorn T (2007). “Boosting Algorithms: Regularization, Prediction and Model Fitting (with Discussion).” Statistical Science, 22(4), 477–505.

Hofner B, Boccuto L, Goeker M (2015). “Controlling false discoveries in high-dimensional situations: Boosting with stability selection.” BMC Bioinformatics, 16(144).

Corresponding BibTeX entries:

  @Article{,
    title = {Model-based Boosting in {R}: A Hands-on Tutorial Using the
      {R} Package mboost},
    author = {Benjamin Hofner and Andreas Mayr and Nikolay Robinzonov
      and Matthias Schmid},
    journal = {Computational Statistics},
    year = {2014},
    volume = {29},
    pages = {3--35},
  }

  @Article{,
    title = {Model-based Boosting 2.0},
    author = {Torsten Hothorn and Peter Buehlmann and Thomas Kneib and
      Matthias Schmid and Benjamin Hofner},
    journal = {Journal of Machine Learning Research},
    year = {2010},
    volume = {11},
    pages = {2109--2113},
  }

  @Article{,
    title = {Boosting Algorithms: Regularization, Prediction and Model
      Fitting (with Discussion)},
    author = {Peter Buehlmann and Torsten Hothorn},
    journal = {Statistical Science},
    year = {2007},
    volume = {22},
    number = {4},
    pages = {477--505},
  }

  @Article{,
    title = {Controlling false discoveries in high-dimensional
      situations: Boosting with stability selection},
    author = {Benjamin Hofner and Luigi Boccuto and Markus Goeker},
    journal = {{BMC} Bioinformatics},
    year = {2015},
    volume = {16},
    number = {144},
  }