Improved Confidence Interval Estimation for Zero-Inflated Count Data Using Transformed Two-Part Bootstrap
Year: 2026
Type: article
Abstract: This study proposes a transformed two-part bootstrap confidence interval (TTB-CI) for zero-inflated count data. The method combines a standard zero-inflated mixture formulation, parametric bootstrap, and monotone transformations to improve inference for practically meaningful estimands, including the marginal mean, zero probability, and positive-part mean. Simulation studies under zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) data-generating processes show that the proposed method maintains nominal or near-nominal coverage while reducing interval width, particularly for the positive-part mean. Compared with conventional Poisson- and negative binomial-based confidence intervals, the proposed TTB-CI provides a more favorable coverage and width tradeoff and yields more informative intervals for positive count inference. These results indicate that the proposed method offers a practical and efficient confidence interval framework for zero-inflated count data.
Source: AppliedMath
Authors: Sangsung Park, Sunghae Jun
Institution: Cheongju University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,207
OpenAlex ID: W7166032941