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A General Framework For Frequentist Model Averaging by Mitra, Priyam; Lian, Heng; Mitra, Ritwik; Liang, Hua; Xie, Min-ge is a scholarly article available to read on EtoBox.

What is A General Framework For Frequentist Model Averaging about?

Model selection strategies have been routinely employed to determine a model for data analysis in statistics, and further study and inference then often proceed as though the selected model were the true model that were known a priori. This practice does not account for the uncertainty introduced by the selection process and the fact that the selected model can possibly be a wrong one. Model averaging approaches try to remedy this issue by combining estimators for a set of candidate models. Specifically, instead of deciding which model is the 'right' one, a model averaging approach suggests to fit a set of candidate models and average over the estimators using certain data adaptive weights. In this paper we establish a general frequentist model averaging framework that does not set any restrictions on the set of candidate models. It greatly broadens the scope of the existing methodologies under the frequentist model averaging development. Assuming the data is from an unknown model, we derive the model averaging estimator and study its limiting distributions and related predictions while taking possible modeling biases into account. We propose a set of optimal weights to combine the

Author

Mitra, Priyam; Lian, Heng; Mitra, Ritwik; Liang, Hua; Xie, Min-ge

Published

2018

Language

EN