Date of Award


Degree Type


Degree Name

Master of Science in Statistics


Computer Science and Statistics

First Advisor

Gavino Puggioni


The objective of this thesis is to explore the relationship between cry acoustic data and neurological development. The methods used in this project were general linear models as well as XGBoost models. Additional methods explored include general linear mixed effects models and combining mixed effects estimation with machine learning algorithms. A description and simulation to compare those methods is provided, and a discussion on when using mixed effects with machine learning could be beneficial. An analysis was carried out to predict autism spectrum disorder using cry recording data from newborn infants, as well as a simulation study to understand how sample size can impact the results of that modeling. Another analysis was carried out to predict 12 month development index from the cry acoustic data. Cry acoustic data seems to decrease out of sample variation when predicting ASD, but not clear advantage is seen when predicting mental development index.

Available for download on Friday, May 17, 2024