Machine Learning
| Hi-index | 0.03 |
Most variable selection techniques focus on first-order linear regression models. Often, interaction and quadratic terms are also of interest, but the number of candidate predictors grows very fast with the number of original predictors, making variable selection more difficult. Forward selection algorithms are thus developed that enforce natural hierarchies in second-order models to control the entry rate of uninformative effects and to equalize the false selection rates from first-order and second-order terms. Method performance is compared through Monte Carlo simulation and illustrated with data from a Cox regression and from a response surface experiment.