Combining models in longitudinal data analysis

Song Liu, Yuhong Yang

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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 languageEnglish (US)
Pages (from-to)233-254
Number of pages22
JournalAnnals of the Institute of Statistical Mathematics
Issue number2
StatePublished - Apr 2012

Bibliographical note

Funding Information:
The work of the second author is partially supported by NSF grant DMS-0706850.


  • Adaptive regression by mixing
  • Longitudinal data
  • Model combining
  • Model selection
  • Model selection diagnostics
  • Model selection uncertainty


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