Nonlinear principal component analysis

Modern Analysis of Customer Surveys: with Applications using R

Volumn , Issue , 2011, Pages 333-356

  Ferrari, Pier Alda ^a   Barbiero, Alessandro B ^a  

^a UNIVERSITY OF MILAN   (Italy)

Author keywords

ABC ACSS data; Customer satisfaction; Missing data; Nonlinear principal component analysis (NLPCA); Ordinal variables; Rasch model

Indexed keywords

EID: 84866932655     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9781119961154.ch17     Document Type: Chapter

References (20)

1. Andrich, D. (1978) A rating formulation for ordered response categories. Psycometrika, 43, 561-573.
2. Bertrand, M. and Mullainathan, S. (2001) Do people mean what they say? Implications for subjective survey data. American Economic Review, 91(2), 67-72.
3. Carpita M. and Manisera M. (2011) On the nonlinearity of homogeneous ordinal variables. In S. Ingrassia, R. Rocci and M. Vichi (eds), New Perspectives in Statistical Modeling and Data Analysis. Berlin: Springer.
4. Cronbach, L.J. (1951) Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297-334.
5. de Leeuw, J. and Mair, P. (2009) Gifi methods for optimal scaling in R: The package homals. Journal of Statistical Software, 31(4).
6. Efron, B. (1979) Bootstrap method: Another look at the jackknife. Annals of Statistics, 7, 1-26.
7. Efron, B. and Tibshirani, R.J. (1993) An Introduction to the Bootstrap. New York: Chapman & Hall.
8. Ferrari, P.A. and Salini, S. (2011), Complementary use of Rasch models and nonlinear principal components analysis in the assessment of the opinion of Europeans about utilities. Journal of Classification, 28(1), 53-69.
9. Ferrari P.A., Annoni P. and Salini S. (2005) A comparison between alternative models for environmental ordinal data: Nonlinear PCA vs Rasch analysis. In A.R. Francis, K.M. Matawie, A. Oshlack and G.K. Smyth (eds), Statistical Solutions to Modern Problems: Proceedings of the 20th International Workshop on Statistical Modelling, pp. 173-177. Sydney: University of Western Sydney.
10. Ferrari, P.A., Annoni, P. and Urbisci, S. (2006) A proposal for setting-up indicators in the presence of missing data: The case of vulnerability indicators. Statistica e Applicazioni 4(1), 73-88.
11. Ferrari, P.A., Annoni, P., Barbiero, A. and Manzi, G. (2011) An imputation method for categorical variables with application to nonlinear principal component analysis. Computational Statistics and Data Analysis, 55(7), 2410-2420.
12. Freeman, R.B. (1978) Job satisfaction as an economic variable. American Economic Review, 68(2), 135-141.
13. Gifi, A. (1990) Nonlinear Multivariate Analysis. New York: John Wiley & Sons, Inc.
14. Guilford, J.P. (1965) Fundamental Statistics in Psychology and Education. New York: McGraw-Hill.
15. Heiser, W.J. and Meulman, J.J. (1994) Homogeneity analysis: Exploring the distribution of variables and their nonlinear relationship. In M.J. Greenacre and J. Blasius (eds), Correspondence Analysis in the Social Sciences: Recent Developments and Applications, pp. 179-209. New York: Academic Press.
16. Likert, R. (1932) A technique for the measurement of attitudes. Archives of Psychology, 140, 595-639.
17. Little, R.J.A. and Rubin, D.B. (2002) Statistical Analysis with Missing Data, 2nd edn. New York: Wiley Interscience.
18. Masters, G.B. (1982) A Rasch model for partial credit scoring. Psychometrika, 47, 149-174.
19. Michailidis, G. and De Leeuw, J. (1998) The Gifi system of descriptive multivariate analysis. Statistical Science, 13, 307-336.
20. Rasch G. (1980) Probabilistic Models for Some Intelligence and Attainment Tests. Chicago: University of Chicago Press.