Confidence Interval Estimation Using Bootstrapping Methods And Maximum Likelihood Estimate
Confidence interval estimation is an important technique to estimate parameter of a population calculated from a sample drawn from the population. The objective of this study is to present the steps to calculate confidence interval using SPSS. The objective of this paper also is to compare confidenc...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/30763/2/ICMS%202021%20249-255.pdf https://repo.uum.edu.my/id/eprint/30763/ https://uitmicms.wixsite.com/icms2021/publication |
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| Summary: | Confidence interval estimation is an important technique to estimate parameter of a population calculated from a sample drawn from the population. The objective of this study is to present the steps to calculate confidence interval using SPSS. The objective of this paper also is to compare confidence interval using maximum likelihood estimate, percentile bootstrap, and bias-corrected and accelerated methods.Bootstrap is not commonly used because this method is complex to calculate. The advantages of bootstrapping are valid for small samples, and it is a convenient tool. The study found that the BCa method produced CIs closer to the desired level of the coverage than the other methods. |
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