Semi-parametric models for binary regression: an application to premature birth data

• Grant number: 23/15010-3
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: January 01, 2024
• End date: December 31, 2024
• Field of knowledge: Physical Sciences and Mathematics - Probability and Statistics - Statistics
• Principal Investigator: Marcio Alves Diniz
• Grantee: Letícia Bernardes Sartori
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil

Abstract

Around the world, thousands of births are classified as premature, often causing irreversible damage to the health of children and/or mothers.This project intends to use statistical techniques, more specifically, the estimation of binary regression models (in which a variable of interest can assume only two values, such as 1, if the child is premature and 0, otherwise) to identify and quantify factors of mother and child that may be associated with prematurity of births.In addition to traditional parametric models, such as logistic regression, we also intend to estimate more flexible semi-parametric models as they are based on Gaussian processes.