Small sample properties of bootstrap
smallsampleprope00bern.pdf (3.568Mb)Abstract
The Bootstrap method is a nonparametric statistical technique for estimating the sampling
distribution of estimators of unknown parameters. While the asymptotic theory for
bootstrap is well established, this thesis investigates the behavior of the bootstrap for
small sample sizes. For the exponential distribution and for normal linear regression the
bootstrap estimates of 'he parameters and their variances are compared with the theoretical
sampling distributions. The small sample properties of bootstrap confidence intervals
using the percentile method and the bias-corrected percentile method are also
investigated.
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