Abstract
The problem of collinearity among right-censored data is considered in multivariate linear regression by combining mean imputation and the Partial Least Squares (PLS) methods. The purpose of this paper is to investigate the performance of PLS regression when explanatory variables are strongly correlated financial ratios. It is shown that ignoring the presence of censoring in the data can cause a bias. The proposed methodology is applied to a data set describing the financial status of some small and medium-sized Tunisian firms. The derived model is interesting to be able to predict the lifetime of a firm until the occurrence of the failure event.
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References
Altman, E. (1968). Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy. Journal of Finance, 23, 589–609.
Bastien, P. (2008). Régression PLS et données censurées. Thése en informatique. Conservatoire National des Arts et Métiers, Paris.
Beaver, W. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111.
Beaver, W. H., McNichols, M. F., & Rhie, J. (2005). Have financial statements become less informative? evidence from the ability of financial ratios to predict bankruptcy. Review of Accounting Studies, 10, 93–122.
Datta, S., Le-Rademacher, J., & Datta, S. (2007). Predicting patient survival from microarray data by accelerated failure time modeling using partial least square and LASSO. Biometrics, 63, 259–271.
Hillegeist, S., Cram, D., Keating, E., & Lundstedt, K. (2004). Assessing the probability of bankruptcy. Review of Accounting Studies, 9(1), 5–34.
Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Application to non orthogonal problems. Technometrics, 12, 69–82.
Hougaard, P. (2003). Analysis of multivariate survival data. Springer.
Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observation. Journal of American Statistical Association, 53, 457–81.
Massy, W.F. (1965). Principal components regression in exploratory statistical research. Journal of American Statistical Association, 60, 234–256.
Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18, 109–131.
Tenenhaus, M. (1998). La régression PLS Théorie et pratique. Paris: Technip.
Tibshirani, R. (1996). Regression shrinkage and selection via the LASSO. Journal of Royal Statistical Society, 58, 267–288.
Wold, H. (1966). Estimation of principal component and related models by iterative least squares in multivariate analysis (pp. 391–420). New York: Academic.
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Mdimagh, I., Benammou, S. (2011). Using Partial Least Squares Regression in Lifetime Analysis. In: Ingrassia, S., Rocci, R., Vichi, M. (eds) New Perspectives in Statistical Modeling and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11363-5_33
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DOI: https://doi.org/10.1007/978-3-642-11363-5_33
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