Re-sampling in Linear Regression Model Using Jackknife and Bootstrap

Re-sampling in Linear Regression Model Using Jackknife and Bootstrap

Statistical inference is based generally on some estimates that are functions of the data. Resampling methods offer strategies to estimate or approximate the sampling distribution of a statistic. In this article, two resampling methods are studied, jackknife and bootstrap, where the main objective i...

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Bibliographic Details
Published in:المجلة العراقية للعلوم الاحصائية Vol. 10; no. 2; pp. 59 - 73
Main Authors: Zakariya Y. Algamal, Khairy B. Rasheed
Format: Journal Article
Language:Arabic
Published: College of Computer Science and Mathematics, University of Mosul 01-12-2010
Online Access:Get full text
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Summary:Statistical inference is based generally on some estimates that are functions of the data. Resampling methods offer strategies to estimate or approximate the sampling distribution of a statistic. In this article, two resampling methods are studied, jackknife and bootstrap, where the main objective is to examine the accuracy of these methods in estimating the distribution of the regression parameters through different sample sizes and different bootstrap replications. Keywords: Jackknife, Bootstrap, Multiple regression, Bias , Variance.
ISSN:1680-855X
2664-2956
DOI:10.33899/iqjoss.2010.28450