Data from a litter matched tumorigenesis experiment arc analysed using a gencralised h e a r mixed model (GLMM) approach to the analysis of clustered survival data in which there is a dependence of failure time observations withii the same litter. Maximum likelihood (ML.) and residual maximum likeli
Reversible jump methods for generalised linear models and generalised linear mixed models
β Scribed by Jonathan J. Forster; Roger C. Gill; Antony M. Overstall
- Publisher
- Springer US
- Year
- 2010
- Tongue
- English
- Weight
- 576 KB
- Volume
- 22
- Category
- Article
- ISSN
- 0960-3174
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
A new weighted orthogonal least squares algorithm is derived to estimate linear and non-linear continuous time differential equation models from complex frequency response data. The algorithm combines the properties and advantages of both weighted and orthogonal least squares algorithms. A weighted
A new estimation procedure for mixed regression models is introduced. It is a development of Henderson's best linear unbiased prediction procedure which uses the joint distribution of the observed dependent random variables and the unknown realisations of the random components of the model. It is pr
## Abstract Residuals are frequently used to evaluate the validity of the assumptions of statistical models and may also be employed as tools for model selection. For standard (normal) linear models, for example, residuals are used to verify homoscedasticity, linearity of effects, presence of outli