On the equivalence of confidence interval estimation based on frequentist model averagingand least-squares for the full model in linear regression

• In many applications of linear regression models, model selection is vital. However, randomness due to model selection is commonly ignored in post-model selection inference. In order to account for the model selection uncertainty in these linear models, least squares frequentist model averaging has been proposed recently. In this paper, we show that the confidence interval from model averaging is asymptotically equivalent to the confidence interval from the full model. Furthermore, we demonstrate that this equivalence also holds in finite samples if the parameter of interest is a linear function of the regression coefficients.

Subject headings

NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)

NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

Keyword

Asymptotic equivalence; Linear model; Local asymptotics; Model selection uncertainty; Post-selection inference

Statistics

Statistik

Publication and Content Type

vet (subject category)

rap (subject category)