IQUIR   05412
INSTITUTO DE QUIMICA ROSARIO
Unidad Ejecutora - UE
artículos
Título:
Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure
Autor/es:
ALLEGRINI, FRANCO; OLIVIERI, ALEJANDRO C.; BRAGA, JEZ W.B.; MOREIRA, ALESSANDRO C.O.
Revista:
ANALYTICA CHIMICA ACTA
Editorial:
ELSEVIER SCIENCE BV
Referencias:
Año: 2018 vol. 1011 p. 20 - 27
ISSN:
0003-2670
Resumen:
A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS).