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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AIP Publishing LLC
2014
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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 |
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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/ |
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13.251813 |