Jackknife And Bootstrap Techniques In The Estimation of regression Parameters

Ijomah Maxwell Azubuike; Chris-Chinedu; Joy Nonso

doi:https://doi.org/10.14445/22315373/IJMTT-V65I12P503

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 65 | Issue 12 | Year 2019 | Article Id. IJMTT-V65I12P503 | DOI : https://doi.org/10.14445/22315373/IJMTT-V65I12P503

Jackknife And Bootstrap Techniques In The Estimation of regression Parameters

Ijomah Maxwell Azubuike, Chris-Chinedu, Joy Nonso

Citation :

Ijomah Maxwell Azubuike, Chris-Chinedu, Joy Nonso, "Jackknife And Bootstrap Techniques In The Estimation of regression Parameters," International Journal of Mathematics Trends and Technology (IJMTT), vol. 65, no. 12, pp. 25-35, 2019. Crossref, https://doi.org/10.14445/22315373/IJMTT-V65I12P503

Abstract

The classifications according to various criteria into successive levels for building a regression model by using jackknife and bootstrap resampling methods were the basis in this study. Bootstrap techniques on the basis ofthe observations and errors resampling re-computing the estimates, while jackknife methodis based on the deleteone and delete-d observations were considered. Wealso estimatedjackknife and bootstrap bias, theconfidence levels and standard errors of the regression coefficients, and to compare them with the concerned estimates of ordinary least squares(OLS). The regression coefficients of jackknife bias, the standard errors and confidence intervals are considerably larger than the bootstrap and the estimated related to OLS standard errors. The bootstrap percentile intervals are smaller than that of thejackknife percentile intervals of the regression coefficients.

Keywords

jackknife, bootstrap, resampling and regression

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