A general framework for frequentist model averaging

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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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. Model averaging approaches, on the other hand, try to combine 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 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 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 individual estimators so that the expected mean squared error of the average estimator is minimized. Simulation studies are conducted to compare the performance of the estimator with that of the existing methods. The results show the benefits of the proposed approach over traditional model selection approaches as well as existing model averaging methods.

Original languageAmerican English
Pages (from-to)205-226
Number of pages22
JournalScience China Mathematics
Issue number2
StatePublished - Feb 1 2019

ASJC Scopus subject areas

  • Mathematics(all)


  • 62F99
  • 62J99
  • asymptotic distribution
  • bias variance trade-off
  • local mis-specification
  • model averaging estima- tors
  • optimal weight selection


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