Ankargren, Sebastian Jin, Shaobo On the least-squares model averaging interval estimator <p>In many applications of linear regression models, randomness due to model selection is commonly ignored in post-model selection inference. In order to account for the model selection uncertainty, least-squares frequentist model averaging has been proposed recently. We show that the confidence interval from model averaging is asymptotically equivalent to the confidence interval from the full model. The finite-sample confidence intervals based on approximations to the asymptotic distributions are also equivalent if the parameter of interest is a linear function of the regression coefficients. Furthermore, we demonstrate that this equivalence also holds for prediction intervals constructed in the same fashion.</p> Asymptotic equivalence;Frequentist model averaging;Linear model;Local asymptotics;Post-selection inference.;62E20;62J05;62J99;62F12;C12;C52;C20 2017-09-06
    https://tandf.figshare.com/articles/journal_contribution/On_the_least_squares_model_averaging_interval_estimator_sup_sup_/4730329
10.6084/m9.figshare.4730329