Shrinkage parameter for modified linear discriminant analysis - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Reports (Research Report) Year : 1992

Shrinkage parameter for modified linear discriminant analysis

Abstract

Linear discriminant analysis is considered in the small-sample, high-dimensional setting. Alternatives, shrinkage estimators, to the usual pooled sample estimate of the covariance matrix are discussed. These estimators are characterized by a shrinkage parameter g taking its values. First, we show that the variance of the modified linear discriminant functions is less than those of the classical linear discriminant function. Morever, we propose two alternative simple procedures, for choosing the shrinkage parameter, which are related the discrimiation problem. Our procedures are based-one on the cross-validated misclassification risk and one on the cross-validated generalized discriminant function as defined in Rayens & Greene (1991). The optimal value of the shrinkage parameter is computed explicity. The efficacy of these methods is examined through some simulation studies.

Domains

Other [cs.OH]
Fichier principal
Vignette du fichier
RR-1793.pdf (676.31 Ko) Télécharger le fichier

Dates and versions

inria-00077033 , version 1 (29-05-2006)

Identifiers

  • HAL Id : inria-00077033 , version 1

Cite

Abdallah Mkhadri. Shrinkage parameter for modified linear discriminant analysis. [Research Report] RR-1793, INRIA. 1992. ⟨inria-00077033⟩
208 View
138 Download

Share

Gmail Facebook Twitter LinkedIn More