For model selection in mixed effects models, Vaida and Blanchard (2005) demonstrated that the marginal Akaike information criterion is appropriate as to the questions regarding the population and the conditional Akaike information criterion is appropriate as to the questions regarding the particular clusters in the data. This article shows that the marginal Akaike information criterion is asymp...

This paper considers a Kullback-Leibler distance (KLD) which is asymptotically equivalent to the KLD by Goutis and Robert [1] when the reference model (in comparison to a competing fitted model) is correctly specified and that certain regularity conditions hold true (ref. Akaike [2]). We derive the asymptotic property of this Goutis-Robert-Akaike KLD under certain regularity conditions. We also...

Data available in software engineering for many applications contains variability and it is not possible to say which variable helps the process of prediction. Most work present defect prediction focused on selection best techniques. For this purpose, deep learning ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning training data par...

Picking one ‘winner’ model for researching a certain phenomenon while discarding the rest implies confidence that may misrepresent evidence. Multimodel inference allows researchers to more accurately represent their uncertainty about which is ‘best’. But multimodel inference, with Akaike weights—weights reflecting relative probability of each candidate model—and bootstrapping, can also be used ...