Edgeworth Expansions for Bootstrapping Regression Models
The Annals of Statistics · 1989 · Volume 17 · Issue 4
Abstract
The asymptotic performance of the bootstrap in linear regression models is studied. Edgeworth expansions show that asymptotically, the bootstrap is always at least as good as, and in some cases better than, the classical normal approximation. The performances of both the bootstrap and the normal approximation depend on the rate of increase in the elements of the design matrix.
Keywords
62E20, 62J05, Asymptotic theory, bootstrap, central limit theorem, Edgeworth expansion, Empirical distribution, Monte Carlo, regression, Resampling
Enable JavaScript for citation tools and additional paper interactions.