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| Reference : Adaptive Bayesian P-splines to estimate varying regression coefficients: application to re... |
| Scientific congresses and symposiums : Paper published in a book | |||
| Physical, chemical, mathematical & earth Sciences : Mathematics Human health sciences : Neurology | |||
| http://hdl.handle.net/2268/27515 | |||
| Adaptive Bayesian P-splines to estimate varying regression coefficients: application to receptor occupancy estimation | |
| English | |
| Jullion, Astrid [ > > ] | |
Lambert, Philippe  [Université de Liège - ULg > Institut des sciences humaines et sociales > Méthodes quantitatives en sciences sociales >] | |
| Vandenhende, François [ > > ] | |
| 2009 | |
| JSM Proceedings, Statistical Computing Section. Alexandria, VA: American Statistical Association. | |
| 4556-4569 | |
| Yes | |
| International | |
| 978-0-9791747-7-3 | |
| Joint Statistical Meeting 2009 | |
| du 1 août au 6 août 2009 | |
| American Statistical Society | |
| International Biometric Society | |
| Institute of Mathematical Statistics | |
| Statistical Society of Canada | |
| International Chinese Statistical Association | |
| International Indian Statistical Association | |
| Washington D.C. | |
| USA | |
| [en] In many applications of linear regression models, the regression coefficients are not regarded
as fixed but as varying with another covariate named the effect modifier. A useful extension of the linear regression models are then varying coefficient models. To link the regression coefficient with the effect modifier, several methods may be considered. Here, we propose to use Bayesian P-splines to relate in a smoothed way the regression coefficient with the effect modifier. We show that this method enables a large level of flexibility: if necessary, adaptive penalties can be introduced in the model (Jullion and Lambert 2007) and linear constraints on the relation between the regression coefficient and the effect modifier may easily be added. We provide an illustration of the proposed method in a PET study where we want to estimate the relation between the Receptor Occupancy and the drug concentration in the plasma. As we work in a Bayesian setting, credibility sets are easily obtained for receptor occupancy, which take into account the uncertainty appearing at all the different estimation steps. | |
| http://hdl.handle.net/2268/27515 |
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