by Mow Wai Ho. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1991. / Bibliographies: leaves 98-104. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Channel Model and Other Basic Assumptions --- p.5 / Chapter 1.2 --- Complexity Measure --- p.8 / Chapter 1.3 --- Maximum Likelihood Sequence Estimator --- p.9 / Chapter 1.4 --- The Viterbi Algorithm ´ؤ An Implementation of MLSE --- p.11 / Chapter 1.5 --- Error Performance of the Viterbi Algorithm --- p.14 / Chapter 1.6 --- Suboptimal Viterbi-like Algorithms --- p.17 / Chapter 1.7 --- Trends of Digital Transmission and MLSE --- p.19 / Chapter 2 --- New Formulation of MLSE --- p.21 / Chapter 2.1 --- The Truncated Viterbi Algorithm --- p.21 / Chapter 2.2 --- Choice of Truncation Depth --- p.23 / Chapter 2.3 --- Decomposition of MLSE --- p.26 / Chapter 2.4 --- Lattice Interpretation of MLSE --- p.29 / Chapter 3 --- The Closest Vector Problem --- p.34 / Chapter 3.1 --- Basic Definitions and Facts About Lattices --- p.37 / Chapter 3.2 --- Lattice Basis Reduction --- p.40 / Chapter 3.2.1 --- Weakly Reduced Bases --- p.41 / Chapter 3.2.2 --- Derivation of the LLL-reduction Algorithm --- p.43 / Chapter 3.2.3 --- Improved Algorithm for LLL-reduced Bases --- p.52 / Chapter 3.3 --- Enumeration Algorithm --- p.57 / Chapter 3.3.1 --- Lattice and Isometric Mapping --- p.58 / Chapter 3.3.2 --- Enumerating Points in a Parallelepiped --- p.59 / Chapter 3.3.3 --- Enumerating Points in a Cube --- p.63 / Chapter 3.3.4 --- Enumerating Points in a Sphere --- p.64 / Chapter 3.3.5 --- Comparisons of Three Enumeration Algorithms --- p.66 / Chapter 3.3.6 --- Improved Enumeration Algorithm for the CVP and the SVP --- p.67 / Chapter 3.4 --- CVP Algorithm Using the Reduce-and-Enumerate Approach --- p.71 / Chapter 3.5 --- CVP Algorithm with Improved Average-Case Complexity --- p.72 / Chapter 3.5.1 --- CVP Algorithm for Norms Induced by Orthogonalization --- p.73 / Chapter 3.5.2 --- Improved CVP Algorithm using Norm Approximation --- p.76 / Chapter 4 --- MLSE Algorithm --- p.79 / Chapter 4.1 --- MLSE Algorithm for PAM Systems --- p.79 / Chapter 4.2 --- MLSE Algorithm for Unimodular Channel --- p.82 / Chapter 4.3 --- Reducing the Boundary Effect for PAM Systems --- p.83 / Chapter 4.4 --- Simulation Results and Performance Investigation for Example Channels --- p.86 / Chapter 4.5 --- MLSE Algorithm for Other Lattice-Type Modulation Systems --- p.91 / Chapter 4.6 --- Some Potential Applications --- p.92 / Chapter 4.7 --- Further Research Directions --- p.94 / Chapter 5 --- Conclusion --- p.96 / Bibliography --- p.104

by Kwan-Moon Leung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves 77-79. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Estimation of the Model with Some Polytomous Entries Missed --- p.5 / Chapter §2.1 --- The Model --- p.5 / Chapter §2.2 --- Full Maximum Likelihood (FML) Estimation --- p.7 / Chapter Chapter 3 --- Estimation of the Model with Some Continuous and Polytomous Entries Missed --- p.13 / Chapter §3.1 --- The Model --- p.13 / Chapter §3.2 --- Pseudo Maximum Likelihood (PsML) Estimation --- p.15 / Chapter Chapter 4 --- Indirect Methods --- p.19 / Chapter §4.1 --- Listwise Deletion Method --- p.19 / Chapter §4.2 --- Mean Imputation Method --- p.19 / Chapter §4.3 --- Regression Imputation Method --- p.20 / Chapter Chapter 5 --- Computation of the Estimates --- p.23 / Chapter §5.1 --- Optimization Procedure --- p.23 / Chapter §5.2 --- Starting Value and Gradient Vector of the Model with Some Polytomous Entries Missed --- p.25 / Chapter §5.3 --- Starting Value and Gradient Vector of the Model with Some Continuous and Polytomous Entries Missed --- p.29 / Chapter Chapter 6 --- Partition Maximum Likelihood (PML) Estimation --- p.35 / Chapter §6.1 --- Motivation --- p.35 / Chapter §6.2 --- PML Procedure of the Model with Some Polytomous Entries Missed --- p.35 / Chapter §6.3 --- PML Procedure of the Model with Some Continuous and Polytomous Entries Missed --- p.37 / Chapter Chapter 7 --- Simulation Studies and Comparison --- p.39 / Chapter §7.1 --- Simulation Study I --- p.39 / Chapter §7.2 --- Simulation Study II --- p.44 / Chapter Chapter 8 --- Summary and Discussion --- p.43 / Tables / Appendix / References

by Lai Siu Wai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1989. / Bibliography: leaves 50-52.

by Chung Sai Ho. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 53-56). / SUMMARY / Chapter 1. --- Introduction --- p.1 / "Functional, Structural and Ultrastructural Relationships" --- p.2 / Identifiability --- p.4 / Non-normally Distributed Regressor --- p.5 / Chapter 2. --- Underlying Non-normality --- p.7 / Beta Regressor and Guassian Errors --- p.8 / Moments --- p.14 / Moment Generating Function & Characteristic Function --- p.17 / Modality --- p.18 / Distribution Portfolio --- p.21 / Chapter 3. --- Modified Maximum Likelihood Estimation --- p.24 / Consistency --- p.26 / Asymptotically Normality --- p.30 / Efficiency of the MMLE --- p.34 / Chapter 4. --- Monte Carlo Simulation Studies --- p.36 / The Use of MMLE --- p.36 / Third Order Moment Estimator with Asymptotically Minimal Variance --- p.42 / Robustness --- p.46 / Chapter 5. --- Discussions and Conclusions --- p.48 / Other Alternatives --- p.48 / Semiparametric and Nonparametric Maximum Likelihood Estimation --- p.51 / References --- p.53

Chung Chi Keung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 80-84). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Partition Maximum Likelihood Estimation of the General Model --- p.5 / Chapter 2.1 --- Introduction --- p.5 / Chapter 2.2 --- Model --- p.5 / Chapter 2.3 --- The Partition Maximum Likelihood Procedure --- p.8 / Chapter 2.3.1 --- PML estimation of pa --- p.9 / Chapter 2.3.2 --- PML estimation of pab --- p.13 / Chapter 2.3.3 --- Asymptotic properties of the first-stage PML estimates --- p.15 / Chapter 3 --- Bayesian Analysis of Stochastic Prior Information --- p.19 / Chapter 3.1 --- Introduction --- p.19 / Chapter 3.2 --- Bayesian analysis of the Model --- p.20 / Chapter 3.2.1 --- "Case 1, Γ = σ2I" --- p.21 / Chapter 3.2.2 --- Case 2，Г as diagonal matrix with different diagonal el- ements --- p.24 / Chapter 3.2.3 --- "Case 3, Г as a general positive definite matrix" --- p.26 / Chapter 4 --- Simulation Design and Numerical Example --- p.29 / Chapter 4.1 --- Simulation Design --- p.29 / Chapter 4.1.1 --- Model --- p.29 / Chapter 4.1.2 --- Methods of evaluation --- p.32 / Chapter 4.1.3 --- Data analysis --- p.33 / Chapter 4.2 --- Numerical Example --- p.34 / Chapter 4.2.1 --- Model --- p.35 / Chapter 5 --- Conclusion and Discussion --- p.42 / APPENDIX I to V --- p.44-50 / TABLES 1 to 10 --- p.51-77 / FIGURES 1 to 3 --- p.78-79 / REFERENCE --- p.80-84

Wong Kwan-to. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 50-52). / Abstracts in English and Chinese. / ACKNOWLEDGMENT --- p.iii / CHAPTER / Chapter ONE --- INTRODUCTION AND LITERATURE REVIEW --- p.1 / Chapter TWO --- THE MODEL --- p.3 / Chapter THREE --- ASYMPTOTIC BEHAVIOR OF THE LEAST SQUARES ESTIMATORS --- p.6 / Chapter FOUR --- EIGHT SPECIAL CASES --- p.12 / Chapter FIVE --- MONTE CARLO EXPERIMENTS --- p.36 / Chapter SIX --- CONCLUSION --- p.40 / APPENDIX --- p.41 / BIBLIOGRAPHY --- p.50

by Mannon Wong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 42-47). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Maximum Likelihood Estimations of Polyserial Correlations --- p.7 / Chapter 3 --- Normal Curvature and the Conformal Normal Curvature of Lo- cal Influence --- p.12 / Chapter 3.1 --- Normal Curvature --- p.14 / Chapter 3.2 --- Conformal Normal Curvature as an Influential Measure --- p.16 / Chapter 4 --- Influential Observations in the Estimations of Polyserial Corre- lations and the Thresholds --- p.18 / Chapter 4.1 --- Case-weights perturbation --- p.18 / Chapter 4.2 --- "Observations Influencing the Estimates of = (μ, Σ, ε，T)" --- p.20 / Chapter 4.3 --- "Observations Influencing the Estimates of θ1 = （(μ, Σ)" --- p.25 / Chapter 4.4 --- Observations Influencing the Estimates of θ2 = （(ε，T) --- p.27 / Chapter 5 --- Examples --- p.28 / Chapter 5.1 --- Cox's Data --- p.28 / Chapter 5.2 --- Aids Data --- p.32 / Chapter 5.3 --- Simulation Data --- p.35 / Chapter 6 --- Discussion --- p.38 / Chapter 7 --- References --- p.42 / Chapter A --- Appendix I --- p.48 / Chapter B --- Appendix II --- p.50 / Chapter C --- Appendix III --- p.73

by Lai-seung Chan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 58-59). / Abstract also in Chinese. / Chapter Chapter 1. --- Introduction --- p.1 / Chapter Chapter 2. --- The Bivariate Model and Maximum Likelihood Estimation --- p.5 / Chapter 2.1 --- The Model --- p.5 / Chapter 2.2 --- Likelihood function of the model --- p.7 / Chapter 2.3 --- Derivatives of likelihood equations --- p.8 / Chapter 2.4 --- Asymptotic properties --- p.11 / Chapter 2.5 --- Optimization procedures --- p.12 / Chapter Chapter 3. --- Generalization to Multivariate Model --- p.14 / Chapter 3.1 --- The Model --- p.14 / Chapter 3.2 --- The Partition Maximum Likelihood (PML) Estimation --- p.15 / Chapter 3.3 --- Asymptotic properties of the PML estimates --- p.19 / Chapter 3.4 --- Optimization procedures --- p.21 / Chapter Chapter 4. --- Simulation Study --- p.22 / Chapter 4.1 --- Designs --- p.22 / Chapter 4.2 --- Results --- p.26 / Chapter Chapter 5. --- Conclusion --- p.30 / Tables --- p.32 / References --- p.58

by Chun-Wai Sit. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 91-92). / Abstract also in Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Ridge Regression --- p.3 / Chapter 1.2 --- Generalized Ridge Regression and Present Work --- p.11 / Chapter Chapter 2 --- Shrinkage Estimation of Regression Coefficients --- p.14 / Chapter 2.1 --- Introduction --- p.15 / Chapter 2.2 --- Dominance over the Least Squares Estimator --- p.17 / Chapter 2.3 --- Dominance over the Ridge Estimator --- p.26 / Chapter 2.4 --- Bayesian Motivation --- p.31 / Chapter 2.5 --- Choosing the Shrinkage Factor --- p.33 / Chapter 2.5.1 --- Generalized Cross-Validation (GCV) --- p.34 / Chapter 2.5.2 --- RIDGM --- p.35 / Chapter 2.5.3 --- Iterative method for selecting the optimum parameter (IA) --- p.38 / Chapter 2.5.4 --- Empirical Bayes Approach --- p.45 / Chapter Chapter 3 --- Simulation Study --- p.47 / Chapter 3.1 --- Simulation Plan --- p.48 / Chapter 3.2 --- Simulation Result --- p.54 / Chapter 3.2.1 --- β = βL --- p.55 / Chapter 3.2.2 --- β = βs --- p.61 / Chapter 3.3 --- Average k and a --- p.67 / Chapter 3.3.1 --- Shrinkage Estimator --- p.67 / Chapter 3.3.2 --- Ridge Estimator --- p.78 / Chapter 3.4 --- Conclusion --- p.88 / References --- p.91

Yiu Choi Fan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 69-71). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Estimation with Known Misclassification Probabilities --- p.7 / Chapter 2.1 --- Model --- p.7 / Chapter 2.2 --- Maximum Likelihood Estimation --- p.9 / Chapter 2.3 --- Standard Errors of the Parameter Estimates --- p.12 / Chapter 3 --- Numerical Examples (I) --- p.13 / Chapter 3.1 --- Analysis of Real Data --- p.13 / Chapter 3.2 --- Analysis of Artificial Data --- p.16 / Chapter 4 --- Simulation Study (I) --- p.19 / Chapter 4.1 --- Simulation Algorithm --- p.19 / Chapter 4.2 --- Simulation Design --- p.20 / Chapter 4.3 --- Reported Statistics --- p.21 / Chapter 4.4 --- Conclusions of Simulation Results --- p.22 / Chapter 5 --- Estimation by Double Sampling Scheme --- p.24 / Chapter 5.1 --- Introduction of Double Sampling Scheme --- p.24 / Chapter 5.2 --- Model --- p.25 / Chapter 5.3 --- Minimum Chi-square Estimation --- p.26 / Chapter 5.4 --- Statistical Properties of the Parameter Estimates --- p.28 / Chapter 6 --- Numerical Examples (II) --- p.30 / Chapter 6.1 --- "Analysis of Real Data, (2x2 Table)" --- p.30 / Chapter 6.2 --- Analysis of Artificial Data (3x3 Table) --- p.32 / Chapter 7 --- Simulation Study (II) --- p.34 / Chapter 7.1 --- Simulation Algorithm --- p.34 / Chapter 7.2 --- Simulation Design --- p.35 / Chapter 7.3 --- Reported Statistics --- p.37 / Chapter 7.4 --- Conclusions of Simulation Results --- p.38 / Chapter 8 --- Conclusions --- p.39 / Appendices --- p.42 / Chapter A.1 --- The proof of the expression for P(Zj = Ehk) --- p.42 / Chapter A.2 --- The proof of puv and whk{uv) --- p.44 / Chapter A.3 --- The proof of the covariance matrix Q --- p.47 / Chapter A.4 --- The proof of the matrix Σ --- p.52 / Tables A1-A9 --- p.54 / Tables B1-B6 --- p.63 / Bibliography --- p.69