Nonlinear Modal Interval Regression for Neonatal Hormone Level Analysis

Sai Yao's research proposes a nonlinear modal interval regression method for analyzing bivariate data dispersion. The method is applied to neonatal hormone data to identify rhythms in cortisol and melatonin levels during the first ten days after birth. The dataset includes supporting files in PDF and TEX formats and is licensed under CC-BY-4.0.

Use Cases

• Modeling the conditional dispersion of data distributions based on covariates.
• Analyzing variability in neonatal hormone levels over time.
• Comparing the accuracy of regression methods for interval estimation.
• Studying the relationship between cortisol and melatonin rhythms in newborns.

Strengths

• The proposed method is validated against conventional modal interval regression and kernel density estimation methods.
• The dataset includes a detailed methodology description in PDF and TEX formats.
• The dataset is small (146.2 KB), facilitating quick download and inspection.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The data's temporal and geographic scope is not specified in the metadata.

Provenance

Source

Sai Yao via figshare.

Collection Method

Likely contains data from neonatal hormone studies, analyzed using kernel density estimation and smoothing splines.

Time Range

Covers the first ten days after birth for the applied example.

Freshness

Last updated 2026-05-25 04:51:57.