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Assessing Local Influence for Specific Restricted Likelihood: Application to Factor Analysis

Published online by Cambridge University Press:  01 January 2025

C. W. Kwan*
Affiliation:
Department of Statistics, The University of Hong Kong
W. K. Fung
Affiliation:
Department of Statistics, The University of Hong Kong
*
Requests for reprints should be sent to W. K. Fung, Department of Statistics, The University of Hong Kong, Pokfulam Road, HONG KONG.

Abstract

In restricted statistical models, since the first derivatives of the likelihood displacement are often nonzero, the commonly adopted formulation for local influence analysis is not appropriate. However, there are two kinds of model restrictions in which the first derivatives of the likelihood displacement are still zero. General formulas for assessing local influence under these restrictions are derived and applied to factor analysis as the usually used restriction in factor analysis satisfies the conditions. Various influence schemes are introduced and a comparison to the influence function approach is discussed. It is also shown that local influence for factor analysis is invariant to the scale of the data and is independent of the rotation of the factor loadings.

Type
Article
Copyright
Copyright © 1998 The Psychometric Society

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Footnotes

The authors are most grateful to the referees, the Associate Editor, and the Editor for helpful suggestions for improving the clarity of the paper.

References

Beckman, R. J., Nachtshiem, C. J., & Cook, R. D. (1987). Diagnostics for mixed-model analysis of variance. Technometrics, 29, 413496.Google Scholar
Billor, N., & Loynes, R. M. (1993). Local influence: A new approach. Communications in Statistics—Theory and Methods, 22, 15951611.CrossRefGoogle Scholar
Cadigan, N. G. (1995). Local influence in structural equation models. Structural Equation Modeling, 2, 1330.CrossRefGoogle Scholar
Castaño-Tostado, E., & Tanaka, Y. (1991). Sensitivity measures of influence on the loading matrix in exploratory factor analysis. Communication in Statistics—Theory and Methods, 20, 13291343.CrossRefGoogle Scholar
Cook, R. D. (1986). Assessment of local influence (with discussion). Journal of the Royal Statistical Society, Series B, 48, 133169.CrossRefGoogle Scholar
Fung, W. K., & Kwan, C. W. (1995). Sensitivity analysis in factor analysis: Difference between using covariance and correlation matrices. Psychometrika, 60, 607614.CrossRefGoogle Scholar
Lawrance, A. J. (1988). Regression transformation diagnostics using local influence. Journal of American Statistical Association, 83, 10671072.CrossRefGoogle Scholar
Lawley, D. N., & Maxwell, A. E.. (1971). Factor analysis as a statistical method (2nd ed.), London: Butterworth.Google Scholar
Mardia, K. V., Kent, J. T., & Bibby, J. M.. (1979). Multivariate analysis, New York: Academic Press.Google Scholar
Tanaka, Y. (1994). Recent advance in sensitivity analysis in multivariate statistical methods. Journal of Japanese Society of Computational Statistics, 7, 125.CrossRefGoogle Scholar
Tanaka, Y., Castaño-Tostado, E., & Odaka, Y. (1990). Sensitivity analysis in factor analysis: Methods and software. In Momirovic, K., & Milder, V. (Eds.), Compstat (pp. 205210), Heidelberg: Physica-Verlag.CrossRefGoogle Scholar
Tanaka, Y., & Odaka, Y. (1989). Influential observations in principal factor analysis. Psychometrika, 54, 475485.CrossRefGoogle Scholar
Tanaka, Y., & Odaka, Y. (1989). Sensitivity analysis in maximum likelihood factor analysis. Communications in Statistics—Theory and Methods, 18, 40674084.CrossRefGoogle Scholar
Tanaka, Y., & Odaka, Y. (1989). Sensitivity analysis in least squares factor analysis. In Diday, E. (Ed.), Data analysis, learning symbolic and numeric knowledge (pp. 141148), New York, Budapest: Nova Science Publishers.Google Scholar
Tanaka, Y., & Watadani, S. (1992). Sensitivity analysis in covariance structure analysis with equality constraints. Communications in Statistics—Theory and Methods, 21, 15011515.CrossRefGoogle Scholar
Thomas, W., & Cook, R. D. (1989). Assessing influence on regression coefficients in generalized linear models. Biometrika, 76, 741749.CrossRefGoogle Scholar
Thomas, W., & Cook, R. D. (1990). Assessing influence on predications from generalized linear models. Technometrics, 32, 5965.CrossRefGoogle Scholar
Wu, X., & Luo, Z. (1993). Second-order approach to local influence. Journal of the Royal Statistical Society, Series B, 55, 929936.CrossRefGoogle Scholar