published_or_final_version / Applied Statistics / Master / Master of Social Sciences

Thesis (M. Soc. Sc.)--University of Hong Kong, 1993. / Includes bibliographical references.

Thesis (M.Soc.Sc.)--University of Hong Kong, 1993. / Includes bibliographical references. Also available in print.

In this thesis, we investigate the estimation problem of treatment effect from Bayesian perspective through which one can first obtain the posterior distribution of unobserved potential outcome from observed data, and then obtain the posterior distribution of treatment effect. We mainly consider how to represent a joint distribution of two potential outcomes - one from treated group and another from control group, which can give us an indirect impression of correlation, since the estimation of treatment effect depends on correlation between two potential outcomes. The first part of this thesis illustrates the effectiveness of adapting Gaussian mixture models in solving the treatment effect problem. We apply the mixture models - Gaussian Mixture Regression (GMR) and Gaussian Mixture Linear Regression (GMLR)- as a potentially simple and powerful tool to investigate the joint distribution of two potential outcomes. For GMR, we consider a joint distribution of the covariate and two potential outcomes. For GMLR, we consider a joint distribution of two potential outcomes, which linearly depend on covariate. Through developing an EM algorithm for GMLR, we find that GMR and GMLR are effective in estimating means and variances, but they are not effective in capturing correlation between two potential outcomes. In the second part of this thesis, GMLR is modified to capture unobserved covariance structure (correlation between outcomes) that can be explained by latent variables introduced through making an important model assumption. We propose a much more efficient Pre-Post EM Algorithm to implement our proposed GMLR model with unobserved covariance structure in practice. Simulation studies show that Pre-Post EM Algorithm performs well not only in estimating means and variances, but also in estimating covariance.

Juesheng Fu. / "April 2003." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (p. 124-132). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web.

by Yiu Ming Cheung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 92-99). / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.1.1 --- Bayesian YING-YANG Learning Theory and Number Selec- tion Criterion --- p.5 / Chapter 1.2 --- General Motivation --- p.6 / Chapter 1.3 --- Contributions of the Thesis --- p.6 / Chapter 1.4 --- Other Related Contributions --- p.7 / Chapter 1.4.1 --- A Fast Number Detection Approach --- p.7 / Chapter 1.4.2 --- Application of RPCL to Prediction Models for Time Series Forecasting --- p.7 / Chapter 1.4.3 --- Publications --- p.8 / Chapter 1.5 --- Outline of the Thesis --- p.8 / Chapter 2 --- Open Problem: How Many Clusters? --- p.11 / Chapter 3 --- Bayesian YING-YANG Learning Theory: Review and Experiments --- p.17 / Chapter 3.1 --- Briefly Review of Bayesian YING-YANG Learning Theory --- p.18 / Chapter 3.2 --- Number Selection Criterion --- p.20 / Chapter 3.3 --- Experiments --- p.23 / Chapter 3.3.1 --- Experimental Purposes and Data Sets --- p.23 / Chapter 3.3.2 --- Experimental Results --- p.23 / Chapter 4 --- Conditions of Number Selection Criterion --- p.39 / Chapter 4.1 --- Alternative Condition of Number Selection Criterion --- p.40 / Chapter 4.2 --- Conditions of Special Hard-cut Criterion --- p.45 / Chapter 4.2.1 --- Criterion Conditions in Two-Gaussian Case --- p.45 / Chapter 4.2.2 --- Criterion Conditions in k*-Gaussian Case --- p.59 / Chapter 4.3 --- Experimental Results --- p.60 / Chapter 4.3.1 --- Purpose and Data Sets --- p.60 / Chapter 4.3.2 --- Experimental Results --- p.63 / Chapter 4.4 --- Discussion --- p.63 / Chapter 5 --- Application of Number Selection Criterion to Data Classification --- p.80 / Chapter 5.1 --- Unsupervised Classification --- p.80 / Chapter 5.1.1 --- Experiments --- p.81 / Chapter 5.2 --- Supervised Classification --- p.82 / Chapter 5.2.1 --- RBF Network --- p.85 / Chapter 5.2.2 --- Experiments --- p.86 / Chapter 6 --- Conclusion and Future Work --- p.89 / Chapter 6.1 --- Conclusion --- p.89 / Chapter 6.2 --- Future Work --- p.90 / Bibliography --- p.92 / Chapter A --- A Number Detection Approach for Equal-and-Isotropic Variance Clusters --- p.100 / Chapter A.1 --- Number Detection Approach --- p.100 / Chapter A.2 --- Demonstration Experiments --- p.102 / Chapter A.3 --- Remarks --- p.105 / Chapter B --- RBF Network with RPCL Approach --- p.106 / Chapter B.l --- Introduction --- p.106 / Chapter B.2 --- Normalized RBF net and Extended Normalized RBF Net --- p.108 / Chapter B.3 --- Demonstration --- p.110 / Chapter B.4 --- Remarks --- p.113 / Chapter C --- Adaptive RPCL-CLP Model for Financial Forecasting --- p.114 / Chapter C.1 --- Introduction --- p.114 / Chapter C.2 --- Extraction of Input Patterns and Outputs --- p.115 / Chapter C.3 --- RPCL-CLP Model --- p.116 / Chapter C.3.1 --- RPCL-CLP Architecture --- p.116 / Chapter C.3.2 --- Training Stage of RPCL-CLP --- p.117 / Chapter C.3.3 --- Prediction Stage of RPCL-CLP --- p.122 / Chapter C.4 --- Adaptive RPCL-CLP Model --- p.122 / Chapter C.4.1 --- Data Pre-and-Post Processing --- p.122 / Chapter C.4.2 --- Architecture and Implementation --- p.122 / Chapter C.5 --- Computer Experiments --- p.125 / Chapter C.5.1 --- Data Sets and Experimental Purpose --- p.125 / Chapter C.5.2 --- Experimental Results --- p.126 / Chapter C.6 --- Conclusion --- p.134 / Chapter D --- Publication List --- p.135 / Chapter D.1 --- Publication List --- p.135

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 120-135).

Thesis (Ph. D.)--University of California, San Diego, 2001. / Vita. Includes bibliographical references (leaves 143-146).

published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

Thesis (Ph. D.)--University of Washington, 1989. / Vita. Includes bibliographical references.