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Estimating bias and variances in bootstrap logistic regression for Umaru and impact data

We employed random-x bootstrap in binary logistic regression model. We investigate the effect of sample size and number of bootstrap replication on the bias and variance. The performance of estimated coefficient is measured based on the bias, variance, and confidence interval of the bootstrap estima...

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Main Authors: Fitrianto, Anwar, Ng, Mei Cing
Format: Conference or Workshop Item
Language:English
Published: AIP Publishing LLC 2014
Online Access:http://psasir.upm.edu.my/id/eprint/57305/1/Estimating%20bias%20and%20variances%20in%20bootstrap%20logistic%20regression%20for%20Umaru%20and%20impact%20data.pdf
http://psasir.upm.edu.my/id/eprint/57305/
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spelling my.upm.eprints.573052017-09-26T04:02:48Z http://psasir.upm.edu.my/id/eprint/57305/ Estimating bias and variances in bootstrap logistic regression for Umaru and impact data Fitrianto, Anwar Ng, Mei Cing We employed random-x bootstrap in binary logistic regression model. We investigate the effect of sample size and number of bootstrap replication on the bias and variance. The performance of estimated coefficient is measured based on the bias, variance, and confidence interval of the bootstrap estimates. In addition, we also focus on the length of confidence interval of the bootstrap estimates. We found that bias and variance decrease for larger sample size. We noticed that length of confidence intervals decrease as the sample size and number of bootstrap replication are getting large. The results show that the estimated coefficient is more precise as the sample size increases. AIP Publishing LLC 2014 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/57305/1/Estimating%20bias%20and%20variances%20in%20bootstrap%20logistic%20regression%20for%20Umaru%20and%20impact%20data.pdf Fitrianto, Anwar and Ng, Mei Cing (2014) Estimating bias and variances in bootstrap logistic regression for Umaru and impact data. In: 3rd International Conference on Quantitative Sciences and Its Applications (ICOQSIA 2014), 12–14 Aug. 2014, Langkawi, Kedah. (pp. 742-747). 10.1063/1.4903665
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description We employed random-x bootstrap in binary logistic regression model. We investigate the effect of sample size and number of bootstrap replication on the bias and variance. The performance of estimated coefficient is measured based on the bias, variance, and confidence interval of the bootstrap estimates. In addition, we also focus on the length of confidence interval of the bootstrap estimates. We found that bias and variance decrease for larger sample size. We noticed that length of confidence intervals decrease as the sample size and number of bootstrap replication are getting large. The results show that the estimated coefficient is more precise as the sample size increases.
format Conference or Workshop Item
author Fitrianto, Anwar
Ng, Mei Cing
spellingShingle Fitrianto, Anwar
Ng, Mei Cing
Estimating bias and variances in bootstrap logistic regression for Umaru and impact data
author_facet Fitrianto, Anwar
Ng, Mei Cing
author_sort Fitrianto, Anwar
title Estimating bias and variances in bootstrap logistic regression for Umaru and impact data
title_short Estimating bias and variances in bootstrap logistic regression for Umaru and impact data
title_full Estimating bias and variances in bootstrap logistic regression for Umaru and impact data
title_fullStr Estimating bias and variances in bootstrap logistic regression for Umaru and impact data
title_full_unstemmed Estimating bias and variances in bootstrap logistic regression for Umaru and impact data
title_sort estimating bias and variances in bootstrap logistic regression for umaru and impact data
publisher AIP Publishing LLC
publishDate 2014
url http://psasir.upm.edu.my/id/eprint/57305/1/Estimating%20bias%20and%20variances%20in%20bootstrap%20logistic%20regression%20for%20Umaru%20and%20impact%20data.pdf
http://psasir.upm.edu.my/id/eprint/57305/
_version_ 1643836447801737216
score 13.211869

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