Model selection and model averaging after multiple imputation

Model selection and model averaging are two important techniques to obtain practical and useful models in applied research. However, it is now well-known that many complex issues arise, especially in the context of model selection, when the stochastic nature of the selection process is ignored and e...

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Veröffentlicht in:	Computational statistics & data analysis 2014-03, Vol.71, p.758-770
Hauptverfasser:	Schomaker, Michael, Heumann, Christian
Format:	Artikel
Sprache:	eng
Schlagworte:	Akaike’s information criterion Bootstrap Computer simulation Confidence intervals Estimates Frequentist model averaging Linear regression Missing data Monte Carlo methods Regression Statistics Survival analysis Uncertainty
Online-Zugang:	Volltext
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