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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Bibliographic Details
Main Authors: Fitrianto Anwar, Cing Ng Mei
Format: Conference Proceeding
Language:English
Subjects:
Bias
Confidence intervals
Regression models
Replication
Statistical analysis
Variance
Online Access:Get full text
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Summary: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.
ISSN:0094-243X
1551-7616
DOI:10.1063/1.4903665