Bootstrap and Jackknife Resampling Algorithms for Estimation of Regression Parameters
Journal Title: Journal of Applied Quantitative Methods - Year 2007, Vol 2, Issue 2
Abstract
In this paper, the hierarchical ways for building a regression model by using bootstrap and jackknife resampling methods were presented. Bootstrap approaches based on the observations and errors resampling, and jackknife approaches based on the delete-one and delete-d observations were considered. And also we consider estimating bootstrap and jackknife bias, standard errors and confidence intervals of the regression coefficients, and comparing with the concerning estimates of ordinary least squares. Obtaining of the estimates was presented with an illustrative real numerical example. The jackknife bias, the standard errors and confidence intervals of regression coefficients are substantially larger than the bootstrap and estimated asymptotic OLS standard errors. The jackknife percentile intervals also are larger than to the bootstrap percentile intervals of the regression coefficients.
Authors and Affiliations
Suat SAHINLER, Dervis TOPUZ
Keywords
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