In this section
@article{Longford:2017:10.5705/ss.202015.0212,
author = {Longford, NT},
doi = {10.5705/ss.202015.0212},
journal = {Statistica Sinica},
pages = {859--877},
title = {Estimation under model uncertainty},
url = {http://dx.doi.org/10.5705/ss.202015.0212},
volume = {27},
year = {2017}
}
TY - JOUR
AB - Model selection has had a virtual monopoly on dealing with model uncertainty ever since models were identified as important conduits for statisticalinference. Model averaging alleviates some of its deficiencies, but does not offer apractical solution in all settings. We propose an alternative based on linear combinations of the candidate models’ estimators. The general proposal is elaboratedfor ordinary regression and is illustrated with examples. Some estimators based oninvalid models contribute to efficient estimation of certain quantities.
AU - Longford,NT
DO - 10.5705/ss.202015.0212
EP - 877
PY - 2017///
SN - 1017-0405
SP - 859
TI - Estimation under model uncertainty
T2 - Statistica Sinica
UR - http://dx.doi.org/10.5705/ss.202015.0212
UR - http://hdl.handle.net/10044/1/32168
VL - 27
ER -
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