RT Generic
T1 A robust partial least squares method with applications
A1 González, Javier
A1 Peña, Daniel
A1 Romera, Rosario
A2 Universidad Carlos III de Madrid. Departamento de Estadística, 
AB Partial least squares regression (PLS) is a linear regression technique developed to relate manyregressors to one or several response variables. Robust methods are introduced to reduce orremove the effect of outlying data points. In this paper we show that if the sample covariancematrix is properly robustified further robustification of the linear regression steps of the PLSalgorithm becomes unnecessary. The robust estimate of the covariance matrix is computed bysearching for outliers in univariate projections of the data on a combination of random directions(Stahel-Donoho) and specific directions obtained by maximizing and minimizing the kurtosiscoefficient of the projected data, as proposed by Peña and Prieto (2006). It is shown that thisprocedure is fast to apply and provides better results than other procedures proposed in theliterature. Its performance is illustrated by Monte Carlo and by an example, where the algorithm isable to show features of the data which were undetected by previous methods.
YR 2007
FD 2007-03
LK https://hdl.handle.net/10016/665
UL https://hdl.handle.net/10016/665
LA eng
LA eng
DS e-Archivo
RD 1 sept. 2024