Multivariate Curve Resolution Homepage

subglobal1 link | subglobal1 link | subglobal1 link | subglobal1 link | subglobal1 link | subglobal1 link | subglobal1 link
subglobal2 link | subglobal2 link | subglobal2 link | subglobal2 link | subglobal2 link | subglobal2 link | subglobal2 link
subglobal3 link | subglobal3 link | subglobal3 link | subglobal3 link | subglobal3 link | subglobal3 link | subglobal3 link
subglobal4 link | subglobal4 link | subglobal4 link | subglobal4 link | subglobal4 link | subglobal4 link | subglobal4 link
subglobal5 link | subglobal5 link | subglobal5 link | subglobal5 link | subglobal5 link | subglobal5 link | subglobal5 link
subglobal6 link | subglobal6 link | subglobal6 link | subglobal6 link | subglobal6 link | subglobal6 link | subglobal6 link
subglobal7 link | subglobal7 link | subglobal7 link | subglobal7 link | subglobal7 link | subglobal7 link | subglobal7 link
subglobal8 link | subglobal8 link | subglobal8 link | subglobal8 link | subglobal8 link | subglobal8 link | subglobal8 link

MCR-WALS - Theory

 

This new version of the MCR-ALS takes into account possible data uncertainties and it presents a more rigorous maximum likelihood least squares approach. Whereas in many chemical applications the assumption of independently identically distributed (iid) noise is reasonable and produces good estimates of the parameters, in other cases of having data with large non-homocedastic noise structures (heterocedastic or correlated noise), MCR-ALS may produce wrong estimates.

The MCR-WALS method is based on a sounder maximum likelihood weighted alternating least squares approach initially proposed by Wentzell et al (BMC Bioinformatics 7 (2006), p. 343) and that it allows for different type of weighting schemes.

© 2010JJ