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Unformatted text preview: BOOTSTRAPPING • In traditional inference, standard errors and confidence intervals are based on the theoretical sampling distribution of parameter estimates (rely on distributional and asymptotic theory) • One way to deal with small samples and non-normality (violations of theoretical sampling distribution) is the bootstrap. • Bootstrap was developed by Efron (1982) o Efron (1982) "The Jackknife, Bootstrap, and Other Resampling Plans". Siam monograph No. 38, CBMS-NSF. Philadelphia o Another reference: Efron, B. and Tibshirani, R. (1993) An introduction to the bootstrap , New York: Chapman and Hall • Provides a way to evaluate the empirical sampling distribution of parameter estimates. This empirical sampling distribution can be used in similar manner to the theoretical sampling distribution. • How does the bootstrap work? 1. Given an observed data set of size n, fit the model and obtain parameter estimates theta. 2. Take a sample of size n from the observed data (with replacement) - this is a bootstrap sample 3. Using the bootstrap sample, fit the model and obtain parameter estimates theta. Using the bootstrap sample, fit the model and obtain parameter estimates theta....
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This note was uploaded on 11/21/2011 for the course PUBH 7435 taught by Professor Melaniewall during the Fall '08 term at Minnesota.
- Fall '08