title: Comparison of partial least squares regression and principal component regression for pelvic shape prediction.
abstract: This paper studied two different regression techniques for pelvic shape prediction, i.e., the partial least square regression (PLSR) and the principal component regression (PCR). Three different predictors such as surface landmarks, morphological parameters, or surface models of neighboring structures were used in a cross-validation study to predict the pelvic shape. Results obtained from applying these two different regression techniques were compared to the population mean model. In almost all the prediction experiments, both regression techniques unanimously generated better results than the population mean model, while the difference on prediction accuracy between these two regression methods is not statistically significant (α=0.01).
authors.affiliation: Institute for Surgical Technology and Biomechanics, University of Bern, Switzerland.
authors.forename: Steffen
authors.initials: S
authors.lastname: Schumann
authors.forename: Lutz-P
authors.initials: LP
authors.lastname: Nolte
authors.forename: Guoyan
authors.initials: G
authors.lastname: Zheng
mesh_terms: D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D010388:Pelvis / Q000033:anatomy & histology*; D025341:Principal Component Analysis; D012044:Regression Analysis