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...
| Main Authors: | , |
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| Format: | Article |
| Language: | Arabic |
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College of Computer Science and Mathematics, University of Mosul
2010-12-01
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| Series: | المجلة العراقية للعلوم الاحصائية |
| Online Access: | https://stats.mosuljournals.com/article_28450_c3732e5fefaa59dec7fa9b836d1afea3.pdf |
| 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. |
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| ISSN: | 1680-855X 2664-2956 |