title: Testing for treatment and interaction effects in semi-parametric analysis of covariance.
abstract: Semi-parametric regression models assume that the effects of covariates on the mean response are additive. We propose a test of additivity when there is one continuous covariate and a group indicator. At p fixed points, the differences of the within-group kernel estimates of the means are calculated, and the likelihood ratio test that the p differences have a constant mean is formed. The kernel bandwidth and the location of the p fixed points are chosen to give the test good power. Performance of the proposed test is compared with parametric and non-parametric tests of additivity. Published in 2001 by John Wiley & Sons, Ltd.
authors.affiliation: National Cancer Institute, National Institutes of Health, USA. hunsber@helix.nih.gov
authors.forename: S
authors.initials: S
authors.lastname: Hunsberger
authors.forename: D A
authors.initials: DA
authors.lastname: Follmann
mesh_terms: D001794:Blood Pressure / Q000502:physiology; D002318:Cardiovascular Diseases / Q000453:epidemiology; D002648:Child; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D015999:Multivariate Analysis; D044469:Racial Groups; D012044:Regression Analysis; D012307:Risk Factors; D014280:Triglycerides / Q000502:physiology
chemical_list: D014280:Triglycerides