Semiparametric regression and graphical models
- Publication Type:
- Journal Article
- Citation:
- Australian and New Zealand Journal of Statistics, 2009, 51 (1), pp. 9 - 41
- Issue Date:
- 2009-03-01
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
 | 2010000467OK.pdf | 929.69 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Semiparametric regression models that use spline basis functions with penalization have graphical model representations. This link is more powerful than previously established mixed model representations of semiparametric regression, as a larger class of models can be accommodated. Complications such as missingness and measurement error are more naturally handled within the graphical model architecture. Directed acyclic graphs, also known as Bayesian networks, play a prominent role. Graphical model-based Bayesian 'inference engines', such as bugs and vibes, facilitate fitting and inference. Underlying these are Markov chain Monte Carlo schemes and recent developments in variational approximation theory and methodology. © 2009 Australian Statistical Publishing Association Inc.
Please use this identifier to cite or link to this item: