We have previously discussed the importance of estimating uncertainty in our measurements and incorporating it into data analysis 1. To know the extent to which we can generalize our observations, we ...

This project focuses on applying bootstrap sampling, a powerful resampling method, to improve the evaluation of machine learning models. The project uses the Scikit-learn Breast Cancer Diagnostics ...

Studies on the iteration procedure in double bootstrap method have given a great impact on confidence interval performance. However, the procedure was claimed to be complicated and demand intensive ...

Abstract: In this paper, the selection of clustering number in K-means clustering algorithm is studied, based on the Bootstrap sampling, a new method is proposed to determine the best clustering ...

The bias-corrected bootstrap confidence interval (BCBCI) was once the method of choice for conducting inference on the indirect effect in mediation analysis due to its high power in small samples, but ...