On High-Dimensional Asymptotic Properties of Model Averaging Estimators
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When multiple models are considered in regression problems, the model averaging method can be used to weigh and integrate the models. In the present study, we examined how the goodness-of-prediction of the estimator depends on the dimensionality of explanatory variables when using a generalization of the model averaging method in a linear model. We specifically considered the case of high-dimensional explanatory variables, with multiple linear models deployed for subsets of these variables. Consequently, we derived the optimal weights that yield the best predictions. we also observe that the double-descent phenomenon occurs in the model averaging estimator. Furthermore, we obtained theoretical results by adapting methods such as the random forest to linear regression models. Finally, we conducted a practical verification through numerical experiments.
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