A simulation-based goodness-of-fit test for random effects in generalized linear mixed models

Scandinavian Journal of Statistics

Volumn 33, Issue 4, 2006, Pages 721-731

  Waagepetersen, Rasmus ^a  

^a AALBORG UNIVERSITY   (Denmark)

Author keywords

Conditional simulation; Empirical distribution function; Generalized linear mixed model; Goodness of fit; Random effects

Indexed keywords

EID: 33750983014     PISSN: 03036898     EISSN: 14679469     Source Type: Journal    
DOI: 10.1111/j.1467-9469.2006.00504.x     Document Type: Article

References (15)

1. Breslow, N. E. & Clayton, D. G. (1993). Approximate inference in generalized linear mixed models. J. Amer. Statist. Assoc. 88, 9-25.
2. Damstrup, M. L. & Nielsen, M. M. (2002). Fytaseaktivitet under rugbrødsfremstilling. Course report. Royal Veterinary and Agricultural University, Copenhagen.
3. Dey, D. K., Gelfand, A. E., Swartz, T. B. & Vlachos, P. K. (1998). A simulation-intensive approach for checking hierarchical models. Test 7, 325-346.
4. Hipel, K. W. & McLeod, A. I. (1994). Time series modelling of water resources and environmental systems. Elsevier, Amsterdam.
5. McCullagh, P. & Neider, J. A. (1989). Generalized linear models, 2nd edn. Chapman and Hall, London.
6. Pierce, D. A. (1982). The asymptotic effect of substituting estimators for parameters in certain types of statistics. Ann. Statist. 10, 475-478.
7. Pinheiro, J. C. & Bates, D. M. (1995). Approximations to the log-likelihood function in the nonlinear mixed-effects model. J. Comput. Graph. Statist. 4, 12-35.
8. R Development Core Team (2004). R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0.
9. Rabe-Hesketh, S., Skrondal, A. & Pickles, A. (2004). Gllamm manual. U.C. Berkeley Division of Biostatistics Working Paper Series 1160. Berkeley Electronic Press. Available at: http://ideas.repec.org/p/bep/ucbbio/1160.html.
10. Ritz, C. (2004). Goodness-of-fit tests for mixed models. Scand. J. Statist. 31, 443-458.
11. Sorensen, D. & Waagepetersen, R. (2003). Normal linear models with genetically structured variance heterogeneity: a case study. Genet. Res. 82, 207-222.
12. Thall, P. F. & Vail, S. C. (1990). Some covariance models for longitudinal count data with overdispersion. Biometrics 46, 657-671.
13. Van der Vaart, A. W. (1998). Asymptotic statistics. Cambridge University Press, Cambridge.
14. Wolfinger, R. D. (1999). Fitting nonlinear mixed models with the new NLMIXED procedure. In Proceedings of the Twenty-Fourth Annual SAS Users Group International Conference, paper 287. SAS Institute Inc., Cary, NC. Available at: http://www2.sas.com/proceedings/sugi24/Proceed.pdf.
15. Zhang, D. & Davidian, M. (2001). Linear mixed models with flexible distributions of random effects for longitudinal data. Biometrics 57, 795-802.