Clustering Analysis of Zernike Coefficients From High Order Aberration Patients

<p>This thesis focuses on clustering fifteen Zernike coefficients using the method of clustering of linear regression models (CLM). EM algorithm is used to infer the maximum likelihood estimate of parameters for each cluster. Bayesian information criterion (BIC) combined with Bootstrapped maximum volume (BMV) criterion are used to determine the number of clusters. The Bootstrap method is used to estimate the uncertainty on the number of clusters. These fifteen Zernike coefficients are clustered into four clusters with a 90% confidence interval of the number of clusters being (2, 5).</p>

Name thesis:degree_name

M.S.P.H.

Level thesis:degree_level

Campus Access Thesis

Discipline thesis:degree_discipline

Epidemiology and Biostatistics

Year

2010

Author dc:creator

• Bao, Weichao

Contributors dc:contributor

• Hongmei Zhang

• Biostatistics
• Physical Sciences and Mathematics
• Statistics and Probability

Statement dc:rights

• © 2010, Weichao Bao

Repository record dc:identifier

https://scholarcommons.sc.edu/etd/134

OAI identifier oai:identifier

oai:scholarcommons.sc.edu:etd-1135