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.
Primary Language | English |
---|---|
Journal Section | Articles |
Authors | |
Publication Date | December 30, 2018 |
Acceptance Date | July 26, 2018 |
Published in Issue | Year 2018 Volume: 1 Issue: 1 |