Bias Reduction Techniques: Bootstrapping and Jackknifing
Some inferential statistical techniques do not require distributional assumptions about the
statistics involved. These modern nonparametric methods use large amounts of computation to
explore the empirical variability of a statistic, rather than making a priori assumptions about this
variability, as is done in the traditional parametric t and z tests.
Bootstrapping: Bootstrapping method is to obtain an estimate by combining estimators to each of
many subsamples of a data set. Often M randomly drawn samples of T observations are drawn
from the original data set of size n with replacement, where T is less n.
Jackknife Estimator: A jackknife estimator creates a series of estimate, from a single data set by
generating that statistic repeatedly on the data set leaving one data value out each time. This
produces a mean estimate of the parameter and a standard deviation of the estimates of the
parameter.
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 Spring '08
 Staff
 Statistics, Bias Reduction Techniques, modern nonparametric methods, welldeveloped distributional theory, traditional parametric inference

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