Bootstrap Methods for Linear Models
We consider linear models with deterministic and random design, respectively. The finite-sample error distribution of the least squares estimator of the vector of regression coefficients is approximated by means of appropriate bootstrap methods. The (asymptotic) validity of these approximations is shown by means of (conditional) multivariate central limit theorems.
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