Optimal Designs for Model Averaging in non-nested Models

Abstract

In this paper we construct optimal designs for frequentist model averaging estimation. We derive the asymptotic distribution of the model averaging estimate with fixed weights in the case where the competing models are non-nested. A Bayesian optimal design minimizes an expectation of the asymptotic mean squared error of the model averaging estimate calculated with respect to a suitable prior distribution. We derive a necessary condition for the optimality of a given design with respect to this new criterion. We demonstrate that Bayesian optimal designs can improve the accuracy of model averaging substantially. Moreover, the derived designs also improve the accuracy of estimation in a model selected by model selection and model averaging estimates with random weights.

A Machine Learning Algorithm for Multi-Source Heterogeneous Data With Block-Wise Missing Information

“…The performance of the proposed Machine Learning Model for 'Heterogeneous data & Weight Algorithm' is analysed with various classifiers. The statistical methods have revealed that penalized regression studies are being used to improve the predictions on outcomes by implementing model averaging [Alhorn et. al., (2021)] ideas.…”

Trendbericht Chemiedidaktik 2021

“…46) Eine weitere Möglichkeit, naturwissenschaftliche Inhalte im Unterricht über Spiele zu vermitteln (Gamification), sind Escape Rooms. [47][48][49][50] Auch in der chemiedidaktischen Forschung scheinen sie angekommen zu sein, so gibt es richten sich neu geförderte chemiedidaktische Teilprojekte in Sonderforschungsbereichen an Schulen, Schülerlabore und die Öffentlichkeit. Aktuelle Forschungsfelder sind hier etwa nachhaltige Energieversorgung, 55) künstliche Photosynthese, 56) Nanomedizin 57) oder Kompositmaterialien für die biomagnetische Diagnose.…”