A stopping rule for structure-preserving variable selection

Statistics and Computing

Volumn 6, Issue 1, 1996, Pages 51-56

  Krzanowski, W J ^a  

^a UNIVERSITY OF EXETER   (United Kingdom)

Author keywords

Principal component analysis; Procrustes rotation; Singular value decomposition; Variable selection

Indexed keywords

EID: 21344470832     PISSN: 09603174     EISSN: None     Source Type: Journal    
DOI: 10.1007/BF00161573     Document Type: Article

References (13)

1. Eastment, H. T. and Krzanowski, W. J. (1982) Cross-validatory choice of the number of components from a principal component analysis. Technometrics, 24, 73-78.
2. Gains, N., Krzanowski, W. J. and Thomson, D. M. H. (1988) A comparison of variable reduction techniques in an attitudinal investigation of meat products. Journal of Sensory Studies, 3, 37-48.
3. Gower, J. C. (1971) Statistical methods of comparing different multivariate analyses of the same data, in Mathematics in the Archaeological and Historical Sciences, F. R. Hodson, D. G. Kendall and P. Tautu (eds), Edinburgh University Press, Edinburgh, pp. 138-149.
4. Jeffers, J. N. R. (1967) Two case studies on the application of principal component analysis. Applied Statistics, 16, 225-236.
5. Jolliffe, I. T. (1972) Discarding variables in principal component analysis. I: Artificial data. Applied Statistics, 21, 160-173.
6. Krzanowski, W. J. (1987a) Selection of variables to preserve multivariate data structure, using principal components. Applied Statistics, 36, 22-33.
7. Krzanowski, W. J. (1987b) Cross-validation in principal component analysis. Biometrics, 43, 575-584.
8. Krzanowski, W. J. (1993) Attribute selection in correspondence analysis of incidence matrices. Applied Statistics, 42, 529-541.
9. Langron, S. P. and Collins, A. J. (1985) Perturbation theory for generalized Procrustes analysis. Journal of the Royal Statistical Society, Series B, 47, 277-284.
10. Manly, B. F. J. (1986) Multivariate Statistical Methods, A Primer. Chapman & Hall, London.
11. Sibson, R. (1978) Studies in the robustness of multidimensional scaling: Procrustes statistics. Journal of the Royal Statistical Society, Series B, 40, 234-238.
12. Sibson, R. (1979) Studies in the robustness of multidimensional scaling: perturbation analysis of classical scaling. Journal of the Royal Statistical Society, Series B, 41, 217-229.
13. Wold, S. (1978) Cross-validatory estimation of the number of components in factor and principal component analysis. Technometrics, 20, 397-405.