Claeskens, G., & Hjort, N. L. (2008). Model selection and model averaging. Cambridge: Cambridge University Press. http://www.cambridge.org/catalogue/catalogue.asp?isbn=9780521852258
Hastie, T., Tibshirani, R., & Wainwright, M. (2015). Statistical learning with sparsity: the lasso and generalizations. Boca Raton, FL: Chapman & Hall/CRC. http://web.stanford.edu/~hastie/StatLearnSparsity/
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with applications in R. New York, NY: Springer. http://www-bcf.usc.edu/~gareth/ISL/
Miller, A. (2002). Subset selection in regression (2nd ed.). Boca Raton, FL: Chapman & Hall/CRC. https://www.crcpress.com/Subset-Selection-in-Regression/Miller/p/book/9781584881711
Murray, K., Heritier, S., & Müller, S. (2013). Graphical tools for model selection in generalized linear models. Statistics in Medicine, 32(25), 4438–4451. DOI:10.1002/sim.5855
Müller, S., & Welsh, A. H. (2010). On model selection curves. International Statistical Review, 78(2), 240–256. DOI:10.1111/j.1751-5823.2010.00108.x
Tarr, G., Müller, S., & Welsh, A. H. (2018). mplot: An R package for graphical model stability and variable selection procedures. Journal of Statistical Software, 83(9), 1–28. DOI:10.18637/jss.v083.i09