Bootstrap-adjusted quasi-likelihood information criteria for mixed model selection

Abstract

Model selection has received much attention and significantly developed in the recent decades.When statistical modeling is utilized to analyze the data set and make predictions, it is always natural to ask whether the candidate model fitted is a good model or not. This question is to be investigated and answered by the process of model selection. The challenge of model selection

Biosecurity and antimicrobial use in broiler farms across nine European countries: toward identifying farm-specific options for reducing antimicrobial usage

“…Subsequently, it increased again to around 12%, but with relatively lower peaks at day 17 and 25, followed by a slowly decreasing trend to 5% till day 30-33. At the usual time of depopulation (i.e., day [30][31][32][33][34][35], there was a new increase close to 10% and at day 40 it dropped close to 0% due to the waiting times before slaughter. This shows that, indeed, the practice of depopulation may in itself introduce a risk.…”

“…From the MDS plot, groups of similar farms were identified using k-means clustering [ 32 ] with the R package ‘mclust’ [ 33 ]. The optimal number of clusters was determined using the Bayesian information criterion (BIC) [ 34 ]. In each cluster, the prototypes for each class were computed (i.e.…”