Thesis (1.4 MB) - 
Free access
Appendix (645 Kb) - Free access
Highlights
Doctoral Thesis
unige:72616 
 |
Tree-based methods for moderated regression with application to longitudinal data |
|
| Author | ||
| Director | ||
| Defense | Thèse de doctorat : Univ. Genève, 2015 - GSEM 8 - 2015/04/17 | |
| Abstract | The dissertation proposes contributions to longitudinal data analysis and moderated regression analysis and is structured into three parts. The first part develops a decorated parallel coordinate plot for longitudinal categorical data, featuring a jitter mechanism revealing the diversity of observed longitudinal patterns, allowing the tracking of each individual pattern and filter options for highlighting typical patterns. The second and the third parts develop semi-parametric methods for moderated regression that combine linear regression models with tree-based algorithms. Specifically, the method of the second part allows fitting tree-structured varying coefficients in multivariate generalized linear mixed models for longitudinal data, and the method of the third part implements coefficient-wise tree-structured varying coefficients in generalized linear models. For general use, the methods are implemented in the freely available packages TraMineR and vcrpart for the statistical software environment R. | |
| Keywords | Longitudinal data analysis — Graphical methods — Regression analysis — Statistical learning — Regression trees — Varying coefficient models — Mixed models — Generalized linear models — Ordinal regression models | |
| Identifiers | URN: urn:nbn:ch:unige-726162 | |
| Full text | ||
| Structures | ||
| Citation (ISO format) | BUERGIN, Reto Arthur. Tree-based methods for moderated regression with application to longitudinal data. Université de Genève. Thèse, 2015. doi: 10.13097/archive-ouverte/unige:72616 https://archive-ouverte.unige.ch/unige:72616 |
1038 hits 
1545 downloads 
Contact an author
Update