Cover Image

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Mokhtar, Siti Fairus, Md Yusof, Zahayu, Sapiri, Hasimah
Format: Conference or Workshop Item
Language:English
Subjects:
T Technology (General)
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
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uum.repo.30763
record_format eprints
spelling my.uum.repo.307632024-04-30T13:09:01Z https://repo.uum.edu.my/id/eprint/30763/ Confidence Interval Estimation Using Bootstrapping Methods And Maximum Likelihood Estimate Mokhtar, Siti Fairus Md Yusof, Zahayu Sapiri, Hasimah T Technology (General) 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. Conference or Workshop Item PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/30763/2/ICMS%202021%20249-255.pdf Mokhtar, Siti Fairus and Md Yusof, Zahayu and Sapiri, Hasimah Confidence Interval Estimation Using Bootstrapping Methods And Maximum Likelihood Estimate. In: The 5 International Conference on Computing, Mathematics and Statistics 2021 (iCMS2021), 4 - 5 August 2021, Universiti Teknologi MARA (UiTM) Kedah, Malaysia. https://uitmicms.wixsite.com/icms2021/publication
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutional Repository
url_provider http://repo.uum.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Mokhtar, Siti Fairus
Md Yusof, Zahayu
Sapiri, Hasimah
Confidence Interval Estimation Using Bootstrapping Methods And Maximum Likelihood Estimate
description 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.
format Conference or Workshop Item
author Mokhtar, Siti Fairus
Md Yusof, Zahayu
Sapiri, Hasimah
author_facet Mokhtar, Siti Fairus
Md Yusof, Zahayu
Sapiri, Hasimah
author_sort Mokhtar, Siti Fairus
title Confidence Interval Estimation Using Bootstrapping Methods And Maximum Likelihood Estimate
title_short Confidence Interval Estimation Using Bootstrapping Methods And Maximum Likelihood Estimate
title_full Confidence Interval Estimation Using Bootstrapping Methods And Maximum Likelihood Estimate
title_fullStr Confidence Interval Estimation Using Bootstrapping Methods And Maximum Likelihood Estimate
title_full_unstemmed Confidence Interval Estimation Using Bootstrapping Methods And Maximum Likelihood Estimate
title_sort confidence interval estimation using bootstrapping methods and maximum likelihood estimate
url 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
_version_ 1797922856014708736
score 13.211869

Similar Items