Multiple cases deletion diagnostics for linear mixed models

Communications in Statistics - Theory and Methods

Volumn 37, Issue 7, 2008, Pages 1071-1084

  Zewotir, T ^a  

^a UNIVERSITY OF KWAZULU NATAL   (South Africa)

Author keywords

Fixed effects; Generalized Cook's distance; Influential observations; One step diagnostics; Random effects; Variance components ratios

Indexed keywords

AEROSOLS; COMPUTATIONAL COMPLEXITY; HEALTH RISKS; MATHEMATICAL MODELS; STATISTICAL METHODS;

FIXED EFFECTS; ONE STEP DIAGNOSTICS; RANDOM EFFECTS; VARIANCE COMPONENTS RATIOS;

DATA REDUCTION;

EID: 39049138996     PISSN: 03610926     EISSN: 1532415X     Source Type: Journal    
DOI: 10.1080/03610920701713229     Document Type: Article

References (24)

1. Beckman, R. J., Nachtsheim, C. J., Cook, R. D. (1987). Diagnostics for mixed model analysis of variance. Technometrics 29:413-426.
2. Belsley, D. A., Kuh, E., Welsch, R. E. (1980). Regression Diagnostics. New York: John Wiley.
3. Chatterjee, S., Hadi, A. S. (1986). Influential observation, high leverage points, and outliers in linear regression (with discussion). Statistical Science 1:379-416.
4. Chatterjee, S., Hadi, A. S. (1988). Sensitivity Analysis in Linear Regression. New York: John Wiley.
5. Christensen, R., Johnson, W., Pearson, L. M. (1992a). Prediction diagnostics for spatial linear models. Biometrika 79:583-591.
6. Christensen, R., Pearson, L. M., Johnson, W. (1992b). Case deletion diagnostics for mixed models. Technometrics 34:38-45.
7. Christensen, R., Johnson, W., Pearson, L. M. (1993). Covariance function diagnostics for spatial linear models. Mathematical Geology 25:145-160.
8. Cook, R. D. (1977). Detection of influential observations in linear regression. Technometrics 19:15-18.
9. Cook, R. D., Weisberg, S. (1980). Characterization of an empirical influence function for detecting influential cases in regression. Technometrics 22:495-508.
10. Cook, R. D., Weisberg, S. (1982). Residual and Influence in Regression. London: Chapman and Hall.
11. Duchateau, L., Janssen, P. (1997). An example based tour in linear mixed models. In Linear Mixed Models in Practice: A SAS Oriented Approach. New York: Springer-Verlag, pp. 11-62.
12. Harville, D. A. (1977). Maximum likelihood approaches to variance component estimation and to related problems (with discussion). Journal of the American Statistical Association 72:320-340.
13. Haslett, S. (1999). A simple derivation of deletion diagnostics for the general linear model with correlated errors. Journal of the Royal Statistical Society (B) 61:603-609.
14. Haslett, J., Hayes, K. (1998). Residuals for the linear model with general covariance structure. Journal of the Royal Statistical Society (B) 60:201-215.
15. Haslett, J., Dillane, D. (2004). Application of "delete = replace" to deletion diagnostics for variance component estimation in the linear mixed model. Journal of the Royal Statistical Society (B) 66:131-143.
16. Hemmerle, W. J., Hartley, H. O. (1973). Computing maximum likelihood estimates for the mixed AOV model using the W transformation. Technometrics 15:819-831.
17. Jennrich, R. I., Sampson, P. F. (1976). Newton-Raphson and related algorithms for maximum likelihood estimation of variance components. Technometrics 18:11-18.
18. Martin, R. J. (1992). Leverage, influence and residuals in regression models when observations are correlated. Communications in Statistics - Theory and Methods 21:1183-1212.
19. McCulloch, C. E., Searle, S. R. (2001). Generalized, Linear and Mixed Models. New York: John Wiley.
20. Robinson, G. K. (1991). That BLUP is a good thing: The estimation of random effects (with discussion). Statistical Science 6:15-51.
21. SAS Institute Inc. (1992). SAS Technical Report P-229. Cary, NC: SAS Institute.
22. Searle, S. R., Casella, G., McCulloch, C. E. (1992). Variance Components. New York: John Wiley.
23. Zewotir, T., Galpin J. S. (2005). Influence diagnostics for linear mixed models. Journal of Data Science 3:153-177.
24. Zewotir, T., Galpin, J. S. (2006). Evaluation of linear mixed model case deletion diagnostic tools by Monte Carlo simulation. Communications in Statistics - Simulation and Computation 35:645-682.