An Asymptotic Theory for Linear Model Selection

@inproceedings{Shao1999AnAT,
  title={An Asymptotic Theory for Linear Model Selection},
  author={Jun Shao},
  year={1999}
}
In the problem of selecting a linear model to approximate the true unknown regression model, some necessary and/or sufficient conditions are established for the asymptotic validity of various model selection procedures such as Akaike’s AIC, Mallows’ Cp, Shibata’s FPEλ, Schwarz’ BIC, generalized AIC, crossvalidation, and generalized cross-validation. It is found that these selection procedures can be classified into three classes according to their asymptotic behavior. Under some fairly weak… CONTINUE READING
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