Stimulus selection based on the maximum Fisher information (MFI) principle enables efficient estimation of participant parameters cognitive models with elaborated experimental tasks. However, in typical applications, a single model is assumed to be true, whereby uncertainty ignored, causing poor performance MFI-based stimulus selection. To address this problem, study proposes model-averaged MFI...

The standard practice of selecting a single model from some class of models and then making inferences based on this model ignores model uncertainty. Ignoring model uncertainty can impair predictive performance and lead to overstatement of the strength of evidence via p-values that are too small. Bayesian model averaging provides a coherent approach for accounting for model uncertainty. A varie...

This paper develops a novel, fully automated forecast averaging scheme which combines LASSO estimation with principal component (PCA). LASSO-PCA (LPCA) explores pool of predictions based on single model but calibrated to windows different sizes. It uses information criteria select tuning parameters and hence reduces the impact researchers’ ad hoc decisions. The method is applied average hourly ...