Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 83-90). Also available in electronic version.

Thesis--M. Phil., University of Hong Kong, 1982.

Thesis (M.S.)--Virginia Polytechnic Institute, 1966. / Vita. Abstract. Includes bibliographical references (leaf 51). Also available via the Internet.

Thesis, M.Phil., University of Hong Kong, 1982. / Also available in print.

Dissertation (PhD)--University of Stellenbosch, 2008. / Bibliography. Also available via the Internet.

Thesis (Ph. D.)--University of Florida, 2004. / Includes vita. Includes bibliographical references. Also available via the World Wide Web (viewed June 30, 2006).

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A preliminary study of the feasibility of using categorical variables in discriminant function analysis was performed. Data including both continuous and categorical variables were used and predictive results examined. The discriminant function techniques were found to be robust enough to include the use of categorical variables. Some problems were encountered with using the trace criterion for selecting the most discriminating variables when these variables are categorical. No monotonic relationship was found to exist between the trace and the number of correct predictions. This study did show that the use of categorical variables does have much potential as a statistical tool in classification procedures. (50 pages)

Chan, Ho Wai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 56). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Model --- p.3 / Chapter 3 --- Implementation by Mx --- p.10 / Chapter 3.1 --- Example 1 --- p.10 / Chapter 3.2 --- Example 2 --- p.22 / Chapter 4 --- Covariance structure analysis --- p.26 / Chapter 5 --- Simulation --- p.29 / Chapter 5.1 --- Simulation 1 --- p.29 / Chapter 5.2 --- Simulation 2 --- p.36 / Chapter 6 --- Discussion --- p.41 / Appendix A: Mx input script for ranking data data with p =4 --- p.43 / Appendix B: Selection matrices for ranking data with p = 4 --- p.47 / Appendix C: Mx input script for ranking data data with p = 3 --- p.50 / Appendix D: Mx input script for p = 4 with covariance structure --- p.53 / References --- p.56