Defense Date
4-1-2015
Graduation Date
Spring 2015
Availability
Immediate Access
Submission Type
thesis
Degree Name
MS
Department
Computational Mathematics
School
McAnulty College and Graduate School of Liberal Arts
Committee Chair
John Kern
Committee Member
Frank D'Amico
Committee Member
James Schreiber
Keywords
Pure sciences, Psychology, Autism, Logistic regression, Rare events
Abstract
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.
Format
Language
English
Recommended Citation
Hunter, J. (2015). Exploring autism prediction through logistic regression analysis with corrections for rare events data (Master's thesis, Duquesne University). Retrieved from https://dsc.duq.edu/etd/674