Clustering Algorithm for Zero-Inflated Data
By: Anusorn Thanataveerat
Publisher: [publisher not identified]
Published: 2020
Place of publication: [New York, N.Y.?]
Language: eng
Description: Zero-inflated data are common in biomedical research. In cluster analysis, the heuristic approach fails to provide inferential properties to the outcome while the existing model-based approach only works in the case of a mixture of multivariate normal. In this dissertation, I developed two new model-based clustering algorithms- the multivariate zero-inflated log-normal and the multivariate zero-inflated Poisson clustering algorithms. I then applied these methods to the questionnaire data and compare the resulting clusters to the ones derived from assuming multivariate normal distribution. Associations between clustering results and clinical outcomes were also investigated.
Notes: Department: Biostatistics.

Thesis advisor: Bin Cheng.

Thesis advisor: Ying Kuen Cheung.

Thesis (Dr.P.H.)--Mailman School of Public Health, Columbia University, 2020.