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Journals / The International Journal of Materials and Engineering Technology / 2018 / Cilt: 1 - Sayı: 1

PARAMETER ESTIMATION FOR LINEAR REGRESSION USING BOOTSTRAP METHOD

Recep BİNDAK
Pages
1–5
Publication Date
2018-12-30
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result, it has been found that even 200 bootstrap repetations may suffice to obtain proper results.

📌 3 Key Takeaways

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Objective & Hypothesis:The bootstrap method firstly was introduced by Efron [1] as a general method for assessing the statistical accuracy of an estimator.
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Key Findings & Data:Bootstrap is a computer-based re-sampling approach and a nonparametric statistical inference method.
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Conclusion & Impact:As a result, it has been found that even 200 bootstrap repetations may suffice to obtain proper results.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Bootstrap method · Resampling · Linear regression

Abstract

The bootstrap method firstly was introduced by Efron [1] as a general method for assessing the statistical accuracy of an estimator. Bootstrap is a computer-based re-sampling approach and a nonparametric statistical inference method. In this study, the use of the Bootstrap method in the parameter estimation of the linear regression is introduced and given a sample application on a real data set. In addition, if the data set contains outliers the effect that occurs in parameter estimation is examined. Confidence intervals and standard errors have been identified for various bootstrap repetitions numbers. As a result, it has been found that even 200 bootstrap repetations may suffice to obtain proper results.

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Cite This Article

BİNDAK, R. (2018). PARAMETER ESTIMATION FOR LINEAR REGRESSION USING BOOTSTRAP METHOD. The International Journal of Materials and Engineering Technology, Cilt: 1 - Sayı: 1, 1–5. https://ajindex.com/articles/806084

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References (1)

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