An adapted version of the element-wise weighted total least squares method for applications in chemometrics

Chemometrics and Intelligent Laboratory Systems

Volumn 85, Issue 1, 2007, Pages 40-46

  Schuermans, M ^a   Markovsky, I ^a   Van Huffel, S ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

EW TLS; Measurement errors; MLPCA; Rank reduction

Indexed keywords

ALGORITHM; ARTICLE; CHEMOMETRICS; MATHEMATICAL MODEL; MAXIMUM LIKELIHOOD METHOD; MONTE CARLO METHOD; NOISE; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL;

EID: 33845499041     PISSN: 01697439     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.chemolab.2006.04.003     Document Type: Article

References (6)

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2. Wentzell P.D., Andrews D.T., Hamilton D.C., Faber K., and Kowalski B.R. Maximum likelihood principal component analysis. J. Chemom. 11 (1997) 339-366
3. Wentzell P.D., and Lohnes M.T. Maximum likelihood principal component analysis with correlated measurement errors: theoretical and practical considerations. Chemometr. Intell. Lab. Syst. 45 (1999) 65-85
4. Premoli A., and Rastello M.L. The parametric quadratic form method for solving problems with element-wise weighting. In: Van Huffel S., and Lemmerling P. (Eds). Total least Squares and Errors-In-Variables Modeling: Analysis, Algorithms and Applications (2002), Kluwer 67-76
5. Markovsky I., Rastello M.L., Premoli A., Kukush A., and Van Huffel S. The element-wise weighted total least squares problem. Comp. Stat. Data Anal. 50 1 (2005) 181-209
6. Magnus J.R., and Neudecker H. Matrix Differential Calculus with Applications in Statistics and Econometrics (1988), John Wiley & Sons