Optimal designs for model averaging in non-nested models
| Autor: | Holger Dette, Kira Alhorn, Kirsten Schorning |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Optimal design FOS: Computer and information sciences Model selection 05 social sciences Bayesian probability Asymptotic distribution 01 natural sciences Nested set model Methodology (stat.ME) 010104 statistics & probability Frequentist inference Bayesian optimal design 0502 economics and business Prior probability Applied mathematics 0101 mathematics Statistics Probability and Uncertainty ddc:510 Statistics - Methodology 050205 econometrics Mathematics |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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