Selection of analytical variables to optimize laboratory efforts in future groundwater studies

[1]  W. Krzanowski,et al.  Cross-Validatory Choice of the Number of Components From a Principal Component Analysis , 1982 .

[2]  K. Voorhees,et al.  Analysis of smoke aerosols from nonflaming combustion by pyrolysis/mass spectrometry with pattern recognition , 1984 .

[3]  L. E. Wangen,et al.  A theoretical foundation for the PLS algorithm , 1987 .

[4]  Wojtek J. Krzanowski,et al.  Cross-Validation in Principal Component Analysis , 1987 .

[5]  S. Wold,et al.  Multi‐way principal components‐and PLS‐analysis , 1987 .

[6]  D. W. Osten,et al.  Selection of optimal regression models via cross‐validation , 1988 .

[7]  John M. Deane,et al.  Testing for redundancy in product quality control test criteria: An application to aviation turbine fuel , 1989 .

[8]  Dennis R. Helsel,et al.  Less than obvious - statistical treatment of data below the detection limit , 1990 .

[9]  P. Buseck,et al.  Multivariate Statistics for Large Data Sets: Applications to Individual Aerosol Particles , 1991 .

[10]  Jiri Janata,et al.  Sensitive layer for electrochemical detection of hydrogen cyanide , 1992 .

[11]  J. Grimalt,et al.  Spatial and temporal variance of hydrocarbon pollution data in a coastal river-influenced sedimentary system , 1992 .

[12]  Joan O. Grimalt,et al.  Source input elucidation in aquatic systems by factor and principal component analysis of molecular marker data , 1993 .

[13]  José Manuel Andrade,et al.  Multivariate selection of variables in industrial quality control: Optimizing aviation fuel final control , 1993 .

[14]  J. Grimalt,et al.  Source input elucidation in polluted coastal systems by factor analysis of sedimentary hydrocarbon data , 1993 .