A note on genetic variance components in mixed models

Genetic Epidemiology

Volumn 24, Issue 4, 2003, Pages 297-301

  Hazelton, Martin L ^a   Gurrin, Lyle C ^a  

^a UNIVERSITY OF WESTERN AUSTRALIA   (Australia)

Author keywords

Bias; Generalized linear model; Markov chain Monte Carlo; Random effects

Indexed keywords

ANALYTICAL PARAMETERS; ARTICLE; DATA ANALYSIS; ERROR; FAMILY; GENETIC MODEL; GENETIC VARIABILITY; INFORMATION PROCESSING; INSTRUMENTATION; MATHEMATICAL ANALYSIS;

DATA INTERPRETATION, STATISTICAL; GENOME COMPONENTS; HUMANS; MODELS, GENETIC; MODELS, STATISTICAL; VARIATION (GENETICS);

GENETTA;

EID: 0037739951     PISSN: 07410395     EISSN: None     Source Type: Journal    
DOI: 10.1002/gepi.10242     Document Type: Article

References (12)

1. Breslow NE, Clayton DG. 1993 Approximate inference in generalized linear mixed models. J Am Stat Assoc 88:9-25.
2. Brooks S 1998. Markov chain Monte Carlo method and its application. Statistician 47:69-100.
3. Burton P, Gurrin L, Sly P. 1998 Extending the simple linear regression model to account for correlated responses - an introduction to generalized estimating equations and multilevel mixed modelling. Stat Med 17:1261-91.
4. Burton P, Tiller K, Gurrin L, Cookson W, Musk A, Palmer L. 1999. Genetic variance components analysis for binary phenotypes using generalized linear mixed models (GLMMs) and Gibbs sampling. Genet Epidemiol 17:118-40.
5. Geweke J. 1992. Evaluating the accuracy of sampling-based approaches to calculation posterior moments. In Bernardo JM, Berger JO, Dawid AP, Smith AFM, editors. Bayesian Statistics 4. Oxford: Oxford University Press. p. 169-93.
6. Gilks W, Roberts G. 1996. Strategies for improving MCMC. In Gilks W, Richardson S, Spiegelhalter D, editors. Markov chain Monte Carlo in practice. London: Chapman & Hall. p 89-114.
7. Gilks W, Thomas A, Spiegelhalter D. 1994. A language and program for complex Bayesian modelling. Statistician 43: 169-78.
8. Longford N. 1993. Random coefficient models. Oxford: Oxford University Press.
9. Scurrah K, Palmer L, Burton P. 2000. Variance components analysis for pedigree-based censored survival data using generalized linear mixed models (GLMMs) and Gibbs sampling in BUGS. Genet Epidemiol 19:127-48.
10. Vieu P. 1996. A note on density mode estimation. Stat Probabil Lett 26:297-307.
11. Wand MP, Jones MC. 1995. Kernel smoothing. London: Chapman & Hall.
12. Zeger S, Karim M. 1991. Generalized linear models with random effects: a Gibbs sampling approach. J Am Stat Assoc 26:79-86.