DeepAI AI Chat
Log In Sign Up

Optimal designs for model averaging in non-nested models

by   Kira Alhorn, et al.

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 and none of these models is correctly specified. 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 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.


page 1

page 2

page 3

page 4


Optimal designs for frequentist model averaging

We consider the problem of designing experiments for the estimation of a...

A General Framework For Frequentist Model Averaging

Model selection strategies have been routinely employed to determine a m...

On the asymptotic distribution of model averaging based on information criterion

Smoothed AIC (S-AIC) and Smoothed BIC (S-BIC) are very widely used in mo...

Frequentist Model Averaging for Global Fréchet Regression

To consider model uncertainty in global Fréchet regression and improve d...

Anytime Tail Averaging

Tail averaging consists in averaging the last examples in a stream. Comm...

Model-robust Bayesian design through Generalised Additive Models for monitoring submerged shoals

Optimal sampling strategies are critical for surveys of deeper coral ree...

A reinforced learning approach to optimal design under model uncertainty

Optimal designs are usually model-dependent and likely to be sub-optimal...