Local influence in linear mixed models

Biometrics

Volumn 54, Issue 2, 1998, Pages 570-582

  Lesaffre, Emmanuel ^a   Verbeke, Geert ^a  

^a UNIVERSITY HOSPITALS LEUVEN   (Belgium)

Author keywords

Diagnostics; Influential subjects; Linear mixed model; Longitudinal data

Indexed keywords

STATISTICS;

ANALYTIC METHOD; CANCER DATA SETS; DATA ANALYTICS; DIAGNOSTIC; INFLUENTIAL SUBJECT; LINEAR MIXED MODELS; LOCAL INFLUENCE; LONGITUDINAL DATA; PROSTATE CANCERS;

DISEASES;

ARTICLE; BIOMETRY; DATA ANALYSIS; PROSTATE CANCER; STATISTICAL ANALYSIS; STATISTICAL MODEL;

EID: 0031744665     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.2307/3109764     Document Type: Article

References (17)

1. Barrett, B. E. and Ling, R. F. (1992). General classes of influence measures for multivariate regression. Journal of the American Statistical Association 87, 184-191.
2. Beckman, R. J., Nachtsheim, C. J., and Cook, R. D. (1987). Diagnostics for mixed-model analysis of variance. Technometrics 29, 413-426.
3. Chatterjee, S. and Hadi, A. S. (1988). Sensitivity Analysis in Linear Regression. New York: Wiley.
4. Christensen, R., Pearson, L. M., and Johnson, W. (1992). Case-deletion diagnostics for mixed models. Technometrics 34, 38-45.
5. Cook, R. D. (1977). Detection of influential observations in linear regression. Technometrics 19, 15-18.
6. Cook, R. D. (1986). Assessment of local influence (with discussion). Journal of the Royal Statistical Society, Series B 48, 133-169.
7. Cook, R. D. and Weisberg, S. (1982). Residuals and Influence in Regression. New York and London: Chapman and Hall.
8. De Gruttola, V., Ware, J. H., and Louis, T. A. (1987). Influence analysis of generalized least squares estimators. Journal of the American Statistical Association 82, 911-917.
9. Harville, D. A. (1974). Bayesian inference for variance components using only error contrasts. Biometrika 61, 383-385.
10. Laird, N. M. and Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics 38, 963-974.
11. Louis, T. A. (1988). General methods for analysing repeated measures. Statistics in Medicine 7, 29-45.
12. Pearson, J. D., Morrell, C. H., Landis, P. K., Carter, H. B., and Brant, L. J. (1994). Mixed-effects regression models for studying the natural history of prostate disease. Statistics in Medicine 13, 587-601.
13. Pregibon, D. (1981). Logistic regression diagnostics. Annals of Statistics 9, 705-724.
14. Searle, S. R., Casella, G., and McCulloch, C. E. (1992). Variance Components. New York: John Wiley and Sons.
15. Verbeke, G. and Lesaffre, E. (1996). Large sample properties of the maximum likelihood estimators in linear mixed models with misspecified random-effects distributions. Technical Report 1996.1, Biostatistical Centre of the Catholic University of Leuven, Belgium.
16. Verbeke, G. and Lesaffre, E. (1997). The effect of misspecifying the random effects distribution in linear mixed models for longitudinal data. Computational Statistics and Data Analysis 23, 541-556.
17. Waternaux, C., Laird, N. M., and Ware, J. H. (1989). Methods for analysis of longitudinal data: Blood lead concentrations and cognitive development. Journal of the American Statistical Association 84, 33-41.