Publication:
High Leverage Points and Outliers in Generalized Linear Models for Ordinal Data

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2010
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Birkhauser Boston
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A generalized hat matrix based on Φ-divergences is proposed to determine how much influence or leverage each data value can have on each fitted value to a generalized linear model for ordinal data. After studying for evidence of points where the data value has high leverage on the fitted value, if such influential points are present, we must still determine whether they have had any adverse effects on the fit. To evaluate it we propose a new family of residuals based on Φ-divergences. All the diagnostic measures are illustrated through the analysis of real data.
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Conference: 12th International Symposium on Applied Stochastic Models and Data Analysis Location: Chania, Greece Date: MAY 29-JUN 01, 2007
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Ali, S.M. and Silvey, S.D. (1966). A general class of coefficients of divergence of one distribution from another. Journal of the Royal Statistical Society B, 26, 131–142. Bedrick, E.J. and Tsai, C. (1993). Diagnostics for binomial response models using power divergence statistics. Computational Statistics and Data Analysis, 15, 381–392. Cook, R.D. (1986). Assessment of local influence (with discussion). Journal of the Royal Statistical Society B, 48, 133–169. Cook, R.D. and Weisberg, S. (1982). Residuals and Influence in Regression. Chapman8Hall, New York. Cramér, H. (1946). Mathematical Methods of Statistics. Princeton University Press, Princeton, NJ. Cressie, N. and Read, T. R. C. (1984). Multinomial goodness-of-fit tests. Journal of the Royal Statistical Society B., 46, 440–464. Fahrmeir, L. and Tutz, G. (2001). Multivariate Statistical Modelling Based on Generalized Linear Models. Springer-Verlag, New York. Green, P.J. (1984). Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant alternatives (with discussion). Journal of the Royal Statistical Society B, 46, 149–192. Harville, D.A. (1997). Matrix Algebra from Statistician’s Perspective. Springer-Verlag, New York. Jorgensen, B. (1983). Maximum likehood estimation and large sample inference for generalized linear and non linear regresion models. Biometrika, 70, 19–28. Kagan, M. (1963). On the theory of Fisher’s amount of information. Sov. Math. Dokl., 4, 991–993. Lesaffre, E. and Albert, A. (1989). Multiple-group logistic regression diagnostics. Applied Statistics, 38, 425–440. Liu, I. and Agresti, A. (2005). The analysis of ordered categorical data: An overview and a survey of recent developments. Test, 14(1), 1–73. McCullagh, P. (1980). Regression models for ordinal data (with discussion). Journal of the Royal Statistical Society B, 42, 109–142. McCullagh, P. and Nelder, J.A. (1989). Generalized Linear Models (2nd ed.). Chapman8Hall, New York. Pardo, L. (2006). Statistical Inference Based on Divergence Measures. Chapman & Hall, New York. Pardo, M.C. (2008). Testing equality restrictions in generalized linear models. Metrika, DOI: 10.1007/500184-009-0275-y. Pregibon, D. (1981). Logistic regression diagnostics. Annals of Statistics, 9, 705–724. Rao, C.R. (1973). Linear Statistical Inference and ITS Applications. John Wiley, New York. Thomas,W. and Cook, R.D. (1990). Assesing influence on predictions from generalized linear models. Technometrics, 32, 59–65. Vajda, I. (1989). Theory of Statistical Inference and Information. Kluwer Academic, Dordrecht. Williams, D.A. (1987). Generalized linear model diagnostics using the deviance and single case deletions. Applied Statistics, 36(2), 181–191.