Exploring autism prediction through logistic regression analysis with corrections for rare events data

The study of rare events data in which observations of non-event outcomes far outnumber event outcomes makes inference under these circumstances quite difficult. Ideally, for a binary dependent variable, one would like sample data to contain enough observations from both outcome categories. With rare events data, however, this is usually impossible and/or costly to achieve with random sampling. This exploratory research aims to find a set of potential predictors that could be used to quantify a person's risk for developing autism spectrum disorder. A more efficient data collection strategy will be employed that allows for a smaller sample size of more meaningful data. Then, a statistical correction to the standard logistic regression model will be applied to yield adjusted predictions that take into account the prevalence of autism cases both in the sample data and in the population of interest.

Name thesis:degree_name

MS

Level thesis:degree_level

Immediate Access

Discipline thesis:degree_discipline

Computational Mathematics

Year dc:date.available

2015

Author dc:creator

• Hunter, Jennifer

Contributors dc:contributor

• John Kern
• Frank D'Amico
• James Schreiber

• Pure sciences
• Psychology
• Autism
• Logistic regression
• Rare events

Language dc:language

English

Repository record dc:identifier

https://dsc.duq.edu/etd/674

OAI identifier oai:identifier

oai:dsc.duq.edu:etd-1690