Research Article Open Access

Bootstrap Method for Dependent Data Structure and Measure of Statistical Precision

T. O. Olatayo, G. N. Amahia and T. O. Obilade

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.

Journal of Mathematics and Statistics
Volume 6 No. 2, 2010, 84-88

DOI: https://doi.org/10.3844/jmssp.2010.84.88

Submitted On: 6 November 2009 Published On: 30 June 2010

How to Cite: Olatayo, T. O., Amahia, G. N. & Obilade, T. O. (2010). Bootstrap Method for Dependent Data Structure and Measure of Statistical Precision. Journal of Mathematics and Statistics, 6(2), 84-88. https://doi.org/10.3844/jmssp.2010.84.88

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Keywords

  • Truncated geometric bootstrap
  • standard error
  • bias
  • coefficient of variation
  • ratio
  • root mean square error and bootstrap-t confidence interval