### The Bootstrap (Advanced Data Analysis from an Elementary Point of View, Lecture 8)

Statisticians quantify uncertainty in inference from random data to
parameters through the sampling distributions of statistical functionals. These
distributions are inaccessible in all but the simplest and most implausible
cases. The bootstrap principle: sampling distributions under a good estimate
of the truth are close to the true sampling distributions. Parametric
bootstrapping: methods for finding standard errors, biases and confidence
intervals, and for performing hypothesis tests. Double-bootstraps. Examples
of parametric distribution with Pareto's law of income inequality.
Non-parametric bootstrapping: using the empirical distribution itself as our
model. The Pareto distribution continued. Bootstrapping regressions:
resampling data-points versus resampling residuals; resampling of residuals
under heteroskedasticity. Examples with homework data. Cautions on
bootstrapping with dependent data. When does the bootstrap fail?

*Comment*: The parts of this article which I
didn't plagiarize for the lecture notes I used for
the homework.

PDF

R

pareto.R, wealth.dat

Advanced Data Analysis from an Elementary Point of View

Posted at February 04, 2011 01:37 | permanent link