Version 1
: Received: 13 February 2023 / Approved: 13 February 2023 / Online: 13 February 2023 (09:50:08 CET)
How to cite:
Bittmann, F. dbs: Stata Command to Compute Double Bootstrap Confidence Intervals. Preprints2023, 2023020213. https://doi.org/10.20944/preprints202302.0213.v1
Bittmann, F. dbs: Stata Command to Compute Double Bootstrap Confidence Intervals. Preprints 2023, 2023020213. https://doi.org/10.20944/preprints202302.0213.v1
Bittmann, F. dbs: Stata Command to Compute Double Bootstrap Confidence Intervals. Preprints2023, 2023020213. https://doi.org/10.20944/preprints202302.0213.v1
APA Style
Bittmann, F. (2023). dbs: Stata Command to Compute Double Bootstrap Confidence Intervals. Preprints. https://doi.org/10.20944/preprints202302.0213.v1
Chicago/Turabian Style
Bittmann, F. 2023 "dbs: Stata Command to Compute Double Bootstrap Confidence Intervals" Preprints. https://doi.org/10.20944/preprints202302.0213.v1
Abstract
Bootstrapping is a flexible, powerful and well-established statistical approach to quantify the uncertainty of virtually any point estimate. While multiple versions of bootstrap confidence intervals are already available in Stata, dbs implements the double (iterated) bootstrap. Instead of relying on parametric assumptions such as the non-parametric resampling bootstrap confidence interval does, it is more flexible and derives critical values directly from that data. To do so, multiple methods are available (analytic approach, double resampling, jackknife estimation). In a comparative simulation study it is empirically demonstrated that the strengths of the double bootstrap are particularly evident for small samples (n < 100) when heteroscedasticity is present. While all other approaches result in undercoverage, only the double bootstrap reaches the target coverage level and hence avoids incorrect statistical conclusions. The computational burden is not even necessarily larger than for other bootstrap approaches.
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
