Michael J. Schell

This paper evaluates synthetic data generation methods using random projection and multivariate similarity assessment in low-dimensional settings to address small-sample challenges in biomedical research.

The paper evaluates a synthetic data generation technique to address the small sample problem by creating statistically similar synthetic populations.

This paper introduces a method to generate synthetic populations from sparse biomedical data, showing they are statistically similar to observed samples and can help overcome small sample size limitations.