bootstrap1 - BOOTSTRAPPING In traditional inference,...

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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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bootstrap1 - BOOTSTRAPPING In traditional inference,...

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