Estimation under model uncertainty
Nicholas T. Longford
Abstract
Model selection has had a virtual monopoly on dealing with model uncertainty
ever since models were identified as important conduits
for statistical inference.
Model averaging alleviates some of its deficiencies, but does not offer
a practical solution in all settings.
We propose an alternative based on linear combinations
of the candidate models' estimators.
The general proposal is elaborated on ordinary regression
and is illustrated on examples.
Some estimators based on invalid models
contribute to efficient estimation of certain quantities.
Statistica Sinica 27, 859-877, 2017.