Abstract
Model selection uncertainty in longitudinal data analysis is often much more serious than that in simpler regression settings, which challenges the validity of drawing conclusions based on a single selected model when model selection uncertainty is high. We advocate the use of appropriate model selection diagnostics to formally assess the degree of uncertainty in variable/model selection as well as in estimating a quantity of interest. We propose a model combining method with its theoretical properties examined. Simulations and real data examples demonstrate its advantage over popular model selection methods.
Original language | English (US) |
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Pages (from-to) | 233-254 |
Number of pages | 22 |
Journal | Annals of the Institute of Statistical Mathematics |
Volume | 64 |
Issue number | 2 |
DOIs | |
State | Published - Apr 2012 |
Bibliographical note
Funding Information:The work of the second author is partially supported by NSF grant DMS-0706850.
Keywords
- Adaptive regression by mixing
- Longitudinal data
- Model combining
- Model selection
- Model selection diagnostics
- Model selection uncertainty