TY - JOUR
T1 - On overfitting and post-selection uncertainty assessments
AU - Hong, L.
AU - Kuffner, T. A.
AU - Martin, R.
N1 - Publisher Copyright:
© 2018 Biometrika Trust.
PY - 2018/3/1
Y1 - 2018/3/1
N2 - In a regression context, when the relevant subset of explanatory variables is uncertain, it is common to use a data-driven model selection procedure. Classical linear model theory, applied naively to the selected submodel, may not be valid because it ignores the selected submodel's dependence on the data. We provide an explanation of this phenomenon, in terms of overfitting, for a class of model selection criteria.
AB - In a regression context, when the relevant subset of explanatory variables is uncertain, it is common to use a data-driven model selection procedure. Classical linear model theory, applied naively to the selected submodel, may not be valid because it ignores the selected submodel's dependence on the data. We provide an explanation of this phenomenon, in terms of overfitting, for a class of model selection criteria.
KW - Akaike information criterion
KW - Bayesian information criterion
KW - Model selection
KW - Regression
UR - https://www.scopus.com/pages/publications/85043278543
U2 - 10.1093/biomet/asx083
DO - 10.1093/biomet/asx083
M3 - Article
AN - SCOPUS:85043278543
SN - 0006-3444
VL - 105
SP - 221
EP - 224
JO - Biometrika
JF - Biometrika
IS - 1
ER -