Extending the Information Partition Function: Modeling Interaction Effects in Highly Multivariate, Discrete Data

Because of the huge amounts of data made available by the technology boom in the late twentieth century, new methods are required to turn data into usable information. Much of this data is categorical in nature, which makes estimation difficult in highly multivariate settings. In this thesis we review various multivariate statistical methods, discuss various statistical methods of natural language processing (NLP), and discuss a general class of models described by Erosheva (2002) called generalized mixed membership models. We then propose extensions of the information partition function (IPF) derived by Engler (2002), Oliphant (2003), and Tolley (2006) that will allow modeling of discrete, highly multivariate data in linear models. We report results of the modified IPF model on the World Health Organization's Survey on Global Aging (SAGE).

Name thesis:degree_name

MS

Grantor dc:publisher

Brigham Young University - Provo

Author dc:creator

• Cannon, Paul C.

• Information Partition Function
• interaction effects
• multivariate analysis
• discrete data
• Natural Language Processing
• Statistics and Probability

Language dc:language

English

Repository record dc:identifier

https://scholarsarchive.byu.edu/etd/1234

OAI identifier oai:identifier

oai:scholarsarchive.byu.edu:etd-2233