title: A generalized discriminant rule when training population and test population differ on their descriptive parameters.
abstract: Standard discriminant analysis methods make the assumption that both the labeled sample used to estimate the discriminant rule and the nonlabeled sample on which this rule is applied arise from the same population. In this work, we consider the case where the two populations are slightly different. In the multinormal context, we establish that both populations are linked through linear mapping. Estimation of the nonlabeled sample discriminant rule is then obtained by estimating parameters of this linear relationship. Several models describing this relationship are proposed and associated estimated parameters are given. An experimental illustration is also provided in which sex of birds that differ morphometrically over their geographical range is to be deterrmined and a comparison with the standard allocation rule is performed. Extension to a partially labeled sample is also discussed.
authors.affiliation: Université de Besançon, UMR CNRS 6623, France. biernac math.univ-fcomte.fr
authors.forename: Christophe
authors.initials: C
authors.lastname: Biernacki
authors.forename: Farid
authors.initials: F
authors.lastname: Beninel
authors.forename: Vincent
authors.initials: V
authors.lastname: Bretagnolle
mesh_terms: D000818:Animals; D001699:Biometry; D001717:Birds / Q000033:anatomy & histology; D016000:Cluster Analysis; D016002:Discriminant Analysis; D005260:Female; D016013:Likelihood Functions; D008297:Male; D015233:Models, Statistical; D012727:Sex Characteristics