Bootstrap Method for Dependent Data Structure and Measure of Statistical Precision
Abstract
Problem statement: This article emphasized on the construction of valid inferential procedures for an estimator θ^ as a measure of its statistical precision for dependent data structure. Approach: The truncated geometric bootstrap estimates of standard error and other measures of statistical precision such as bias, coefficient of variation, ratio and root mean square error are considered. Results: We extend it to other measures of statistical precision such as bootstrap confidence interval for an estimator θ^ and illustrate with real geological data. Conclusion/Recommendations: The bootstrap estimates of standard error and other measures of statistical accuracy such as bias, ratio, coefficient of variation and root mean square error reveals the suitability of the method for dependent data structure.
DOI: https://doi.org/10.3844/jmssp.2010.84.88
Copyright: © 2010 T. O. Olatayo, G. N. Amahia and T. O. Obilade. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- 3,981 Views
- 2,992 Downloads
- 2 Citations
Download
Keywords
- Truncated geometric bootstrap
- standard error
- bias
- coefficient of variation
- ratio
- root mean square error and bootstrap-t confidence interval