## Abstract Complex genetic traits are inherently heterogeneous, i.e., they may be caused by different genes, or nonβgenetic factors, in different individuals. So, for mapping genes responsible for these diseases using linkage analysis, heterogeneity must be accounted for in the model. Heterogeneit
Criticism of a hierarchical model using Bayes factors
β Scribed by James H. Albert
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 171 KB
- Volume
- 18
- Category
- Article
- ISSN
- 0277-6715
No coin nor oath required. For personal study only.
β¦ Synopsis
This paper analyses a data file of heart transplant surgeries performed in the United States over a two-year period. A Poisson/gamma exchangeable model is used to learn about the underlying death rates for 94 hospitals. There are concerns about the suitability of this hierarchical model, including the need for a hierarchical structure, the existence of outliers, the choice of prior hyperparameters, the need for a covariate in the model, and the manner in which exchangeability was modelled. Each concern motivates the construction of alternative models and Bayes factors are used to compare the existing model with the alternative models. Graphical displays are used to check the sensitivity of the posterior analysis with respect to model perturbations and plots of Bayes factors are used to criticize these perturbations.
π SIMILAR VOLUMES
Drained triaxial tests are conducted on natural and reinforced sand under various stress paths. Direct shear tests and pull-out tests are conducted on soil-reinforcement interface and on reinforcement, respectively. The effects of two types of reinforcement, viz, woven and non-woven geotextile and n
## Abstract A model for the transmission of the CGG repeat sequence associated with the fragileβX dynamic mutation in the FMR1 gene is developed. The model incorporates both haplotype and family effects on the expansion rate of the sequence. The resulting random effects model is fitted to new data,
A case is discussed where a failure to adequately criticize an ARIMA model led to erroneous inferences about the process underlying the data. A followup analysis, which permitted model criticism, suggested a different interpretation. The case is suggested for classroom presentation.