Optimal designs for model averaging in non-nested models
- 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.
| Author: | Kira AlhornGND, Holger DetteORCiDGND, Kirsten SchorningGND |
|---|---|
| URN: | urn:nbn:de:hbz:294-97136 |
| DOI: | https://doi.org/10.1007/s13171-020-00238-9 |
| Parent Title (English): | Sankhya : A |
| Publisher: | Springer India |
| Document Type: | Article |
| Language: | English |
| Date of Publication (online): | 2023/03/01 |
| Date of first Publication: | 2021/03/01 |
| Publishing Institution: | Ruhr-Universität Bochum, Universitätsbibliothek |
| Tag: | Bayesian optimal design; Model averaging; Model selection; Model uncertainty; Optimal design; Uniform weighting |
| Volume: | 83 |
| Issue: | 2 |
| First Page: | 745 |
| Last Page: | 778 |
| Note: | Dieser Beitrag ist auf Grund des DEAL-Springer-Vertrages frei zugänglich. |
| Dewey Decimal Classification: | Naturwissenschaften und Mathematik / Mathematik |
| open_access (DINI-Set): | open_access |
| faculties: | Fakultät für Mathematik |
| Licence (English): |  Creative Commons - CC BY 4.0 - Attribution 4.0 International |
