Comparison of partial least squares regression and principal component regression for pelvic shape prediction.

Schumann, Steffen; Nolte, Lutz-Peter; Zheng, Guoyan (2013). Comparison of partial least squares regression and principal component regression for pelvic shape prediction. Journal of biomechanics, 46(1), pp. 197-199. Elsevier 10.1016/j.jbiomech.2012.11.005

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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).

Item Type:	Journal Article (Original Article)
Division/Institute:	04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB [discontinued]
UniBE Contributor:	Schumann, Steffen, Nolte, Lutz-Peter, Zheng, Guoyan
Subjects:	500 Science > 570 Life sciences; biology 600 Technology > 610 Medicine & health
ISSN:	0021-9290
Publisher:	Elsevier
Language:	English
Submitter:	Guoyan Zheng
Date Deposited:	12 Jun 2014 16:06
Last Modified:	05 Dec 2022 14:31
Publisher DOI:	10.1016/j.jbiomech.2012.11.005
PubMed ID:	23174420
URI:	https://boris.unibe.ch/id/eprint/46481