FSR Methods for Second-Order Regression Models.

TitleFSR Methods for Second-Order Regression Models.
Publication TypeJournal Article
Year of Publication2011
AuthorsCrews, Hugh B., Dennis D. Boos, and Leonard A. Stefanski
JournalComput Stat Data Anal
Date Published2011 Jun 01

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.

Alternate JournalComput Stat Data Anal
Original PublicationFSR methods for second-order regression models.
PubMed ID21479118
PubMed Central IDPMC3072220
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
P01 CA142538-01 / CA / NCI NIH HHS / United States
R01 CA085848 / CA / NCI NIH HHS / United States