Comparaison de variantes de régressions logistiques PLS et de régression PLS sur variables qualitatives : application aux données d’allélotypage

Content Provider	Semantic Scholar
Author	Meyer, Nicolas G. Maumy-Bertrand, Myriam Bertrand, Frédéric
Copyright Year	2010
Abstract	A microsatellite is a non-coding DNA sequence. Allelotyping consists in establishing the normal or altered status of a set of predefined microsatellites, generaly in a tumor cell. Allelotyping data thus gather a series of binary variables that describes the global state of the cell chromosomes. These binary data are generaly used to explain a characteristic, binary also, of the subject or of the tumor. Allelotyping data are characterised by their number of variables (microsatellites) being sometimes larger than the number of subjects and by the possible collinearity of two microsatellites. The understanding of cancerogenesis mechanisms implies also a multivariate description of the data. The statistical processing of these data thus suggest using PLS regression. PLS variants of linear and logistic regression make no assumptions on the type of data on which the model can be run. In the literature, models theoretically devised for continuous data are sometimes used on binary data. The absence of assumption on data implies that the models be validated using either a bootstrap or a cross-validation method. We compare here the performances of linear and logistic regression on qualitative data.
Starting Page	1
Ending Page	18
Page Count	18
File Format	PDF HTM / HTML
Volume Number	151
Alternate Webpage(s)	http://journal-sfds.fr/article/download/47/38
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article