References
- Aiken, L. S., S. G. West, and R. R. Reno. 1991. Multiple regression: Testing and interpreting interactions. Sage Publications.
- Bauer, D. J., and P. J. Curran. 2005. Probing interactions in fixed and multilevel regression: Inferential and graphical techniques. Multivariate Behavioral Research 40 (3):373–400. doi:https://doi.org/10.1207/s15327906mbr4003_5.
- Cabrera, J., and A. McDougall. 2002. Statistical consulting. New York, NY: Springer.
- Hastie, T., and R. Tibshirani. 1990. Generalized additive models. Chapman & Hall/CRC.
- Hong, T. M., Gui, M. E. Baran, and H. L. Willis. 2010. Modeling and forecasting hourly electric load by multiple linear regression with interactions. Paper presented at the IEEE PES General Meeting, Providence, RI, pp. 1–8, July 25–29. doi:https://doi.org/10.1109/PES.2010.5589959.
- Johnson, P. O., and L. C. Fay. 1950. The Johnson-Neyman technique, its theory and application. Psychometrika 15 (4):349–67. doi:https://doi.org/10.1007/BF02288864.
- Joseph, P. J., K. Vaswani, and M. J. Thazhuthaveetil. 2006. Construction and use of linear regression models for processor performance analysis. Paper presented at the Twelfth International Symposium on High-Performance Computer Architecture, Austin, TX, pp. 99–108. February 11–15. doi:https://doi.org/10.1109/HPCA.2006.1598116.
- Loh, W.-Y. 2002. Regression trees with unbiased variable selection and interaction detection. Statistica Sinica 12:361–86. doi:https://doi.org/10.2307/24306967.URL http://www.jstor.org/stable/24306967.
- Lou, Y., R. Caruana, J. Gehrke, and G. Hooker. 2013. Accurate intelligible models with pairwise interactions. Paper presented at the Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ‘13, New York, NY, pp. 623–631. doi:https://doi.org/10.1145/2487575.2487579.
- Preacher K. J. 2003. A primer on interaction effects in multiple linear regression. http://www.quantpsy.org/interact/interactions.htm.
- Preacher, K. J., P. J. Curran, and D. J. Bauer. 2006. Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis. Technical Report 3. http://www.quantpsy.org/.
- Sorokina, D., R. Caruana, M. Riedewald, and D. Fink. 2008. Detecting statistical interactions with additive groves of trees. Paper presented at the Proceedings of the 25th International Conference on Machine Learning - ICML ‘08, New York, NY, pp. 1000–1007. doi:https://doi.org/10.1145/1390156.1390282.
- Wood, S. N. 2006. Generalized additive models. Chapman and Hall/CRC. doi:https://doi.org/10.1201/9781420010404 URL https://www.taylorfrancis.com/books/9781420010404.
- Zhang, Z. H., Seibold, M. V. Vettore, W.-J. Song, and V. François. 2018. Subgroup identification in clinical trials: An overview of available methods and their implementations with R. Annals of Translational Medicine 6 (7):122. URL http://www.ncbi.nlm.nih.gov/pubmed/29955582 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC6015941. doi:https://doi.org/10.21037/atm.2018.03.07.