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.
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Liu, S., Yang, Y. Combining models in longitudinal data analysis. Ann Inst Stat Math 64, 233–254 (2012). https://doi.org/10.1007/s10463-010-0306-5
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DOI: https://doi.org/10.1007/s10463-010-0306-5