Bias Reduction Techniques: Bootstrapping and Jackknifing Some inferential statistical techniques do not require distributional assumptions about the statistics involved. These modern non-parametric 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 sub-samples 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. Monte Carlo simulation allows for the evaluation of the behavior of a statistic when its
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