Missing-data theory in the context of exploratory data analysis

Chemometrics and Intelligent Laboratory Systems

Volumn 103, Issue 1, 2010, Pages 8-18

  Camacho, José ^a  

^a UNIVERSITY OF GRANADA   (Spain)

Author keywords

Correlation matrix; Data understanding; Exploratory data analysis; Latent structures; Missing data; Rotation; Variable selection

Indexed keywords

ACCURACY; ARTICLE; CHEMOMETRICS; DATA ANALYSIS; EXPLORATORY RESEARCH; FACTORIAL ANALYSIS; LATENT STRUCTURE ANALYSIS; MATHEMATICAL MODEL; PARTIAL LEAST SQUARES REGRESSION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; SIMULATION;

EID: 77954689842     PISSN: 01697439     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.chemolab.2010.04.017     Document Type: Article

References (25)

1. Song Y., Nie F., Zhang C., Xiang S. A unified framework for semi-supervised dimensionality reduction. Pattern Recognition 2008, 41:2789-2799.
2. Zhao X., Liu Y. Generative tracking of 3d human motion by hierarchical annealed genetic algorithm. Pattern Recognition 2008, 41:2470-2483.
3. Alaa E., Hasan D. Face recognition system based on pca and feedforward neural networks. Lecture notes in computer science 2005, 935-942. ISSN 0302-9743.
4. He H., Yu X. A comparison of pca/ica for data preprocessing in remote sensing imagery classification. MIPPR 2005: Image Analysis Techniques, Proceedings of the SPIE 2005, Volume 6044:60-65. D. Li, H. Ma (Eds.).
5. Kourti T., MacGregor J. Multivariate spc methods for process and product monitoring. Journal of Quality Technology 1996, 28(4).
6. Ferrer A. Multivariate statistical process control based on principal component analysis (mspc-pca): some reflections and a case study in an autobody assembly process. Quality Engineering 2007, 19(4):311-325.
7. Jackson J. A User's Guide to Principal Components 2003, Wiley-Interscience, England.
8. Wold S. Cross-validatory estimation of the number of components in factor and principal components. Technometrics 1978, 20(4):397-405.
9. J. Camacho, J. Picó, A. Ferrer, Cross-validation in pca models with the element-wise k-fold (ekf) algorithm: Part i - understanding the beast, Submitted to Chemometrics and Intelligent Laboratory Systems.
10. J. Camacho, J. Picó, A. Ferrer, Cross-validation in pca models with the element-wise k-fold (ekf) algorithm: Part ii - extensions and applications, Submitted to Chemometrics and Intelligent Laboratory Systems.
11. Fabrigar L., Wegener D., MacCallum R., Strahan E. Evaluating the use of exploratory factor analysis in psychological research. Psycological Methods 1999, 4(3):272-299.
12. Costello A., Osborne J. Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis, Practical Assessment. Research & Evaluation 2005, 10(7):1-9.
13. I. Jolliffe, Principal component analysis, Springer Verlag Inc., EEUU, 2002.
14. Kosanovich K., Dahl K., Piovoso M. Improved process understanding using multiway principal component analysis. Engineering Chemical Research 1996, 35:138-146.
15. Gabriel K. The biplot graphic display of matrices with application to principal component analysis. Biometrika 1971, 58:453-467.
16. Jolliffe I. Rotation of principal components: choice of normalization constraints. Journal of Applied Statistics 1995, 22(1):29-35.
17. Nelson P., Taylor P., MacGregor J. Missing data methods in pca and pls: score calculations with incomplete observations. Chemometrics and Intelligent Laboratory Systems 1996, 35:45-65.
18. Andrews D., Wentzell P. Applications of maximum likelihood principal component analysis: incomplete data sets and calibration transfer. Analytica Chimica Acta 1997, 350:341-352.
19. Walczak B., Massart D. Dealing with missing data: Part I. Chemometrics and Intelligent Laboratoy Systems 2001, 58:15-27.
20. Arteaga F., Ferrer A. Dealing with missing data in mspc: several methods, different interpretations, some examples. Journal of Chemometrics 2002, 16:408-418.
21. Arteaga F., Ferrer A. Framework for regression-based missing data imputation methods in on-line mspc. Journal of Chemometrics 2005, 19:439-447.
22. Reis M., Saraiva P. Heteroscedastic latent variable modelling with applications to multivariate statistical process control. Chemometrics and Intelligent Laboratoy Systems 2006, 80:57-66.
23. Wiklund S., Nilsson D., Eriksson L., ostr om M.S., Wold S., Faber K. A randomization test for pls component selection. Journal of Chemometrics 2007, 21:427-439.
24. Camacho J., Picó J., Ferrer A. Data understanding with pca: structural and variance information plots. Chemometrics and Intelligent Laboratory Systems 2010, 100(1):48-56.
25. Wise B., Gallagher N., Bro R., Shaver J., Windig W., Koch R. PLSToolbox 3.5 for Use with Matlab 2005, Eigenvector Research Inc.