Regensburg Pediatric Appendicitis Data with Multiplicative Distortion Error Analysis

A 252.1 KB research paper and associated materials from figshare, authored by Hongyu Cheng and last updated on May 9, 2026. It presents a methodological framework for estimating the multivariate coefficient of variation under multiplicative distortion measurement errors. The approach is validated using simulated data and a real-world analysis of the Regensburg Pediatric Appendicitis dataset.

Use Cases

• Testing new estimators for multivariate coefficients of variation based on the described calibration techniques.
• Benchmarking statistical methods for robustness against multiplicative distortion errors using the provided simulation framework.
• Analyzing clinical datasets, like pediatric appendicitis data, where unobservable variables with measurement error are a concern.

Strengths

• Theoretical foundation includes rigorous asymptotic properties for the proposed estimators.
• Methods are validated through extensive simulations and a real-world clinical dataset analysis.
• Includes multiple inference methods, such as confidence intervals based on asymptotic normality and empirical likelihood.

Limitations

• The dataset's row count and column structure are unknown, limiting suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The primary files are PDF and TEX formats, which may require extraction to access underlying data or code.

Provenance

Source

figshare, author Hongyu Cheng.

Collection Method

Likely contains results from statistical simulations and analysis of the referenced Regensburg Pediatric Appendicitis dataset.

Freshness

Last updated 2026-05-09 18:06:20; freshness should be verified.