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This project discusses the intensity and survival probability derived from Credit Default Swaps (CDS). We utilize two models, the reduced intensity model and the Shift Square Root Diffusion (SSRD) model. In the reduced intensity model, we assume a deterministic intensity and implement a computer simulation to derive the survival probability and intensity from the CDS market quotes of the company. In the SSRD model, the interest rate and intensity are both stochastic and correlated. We discuss the impaction of correlation on the interest rate and intensity. We also conduct a Monte Carlo simulation to determine the dynamics of stochastic interest rate and intensity.

MSc (Med), Population-Based Epidemiology, Faculty of Health Sciences, University of the Witwatersrand, 2010

by Chan Chun-wai, Francis. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves 105-116). / Abstract --- p.I / Acknowledgments --- p.III / Abbreviations --- p.IV / Objectives --- p.VI / Content --- p.VII / Chapter Section 1 --- Introduction / Chapter I. --- Preamble --- p.1 / Chapter II. --- Characteristics of Cell Death Process --- p.1 / Chapter II.1. --- Necrosis --- p.1 / Chapter II.2. --- Apoptosis-Programmed Cell Death --- p.5 / Chapter III. --- Triggering of Programmed Cell Death --- p.10 / Chapter IV. --- DNA Fragmentation and Activation of Endogenous Endonuclease --- p.12 / Chapter V. --- Signal Transduction Leading to Programmed Cell Death --- p.14 / Chapter V.1. --- Role of Calcium Ion --- p.14 / Chapter V.2. --- Role of Protein Kinase C --- p.15 / Chapter V.3. --- Protein Dephosphorylation by Phosphatases --- p.16 / Chapter V.4. --- Role of Adenosine 3':5'-cyclic Monophosphate --- p.17 / Chapter V.5. --- Other Signaling Mechanisms --- p.17 / Chapter VI. --- Gene Regulation in Programmed Cell Death --- p.19 / Chapter VI. 1. --- Gene Expression in Programmed cell death --- p.19 / Chapter VI. 1.1 . --- Tissue Transglutaminase --- p.19 / Chapter VI. 1.2. --- Poly (ADP-ribose) Polymerase --- p.20 / Chapter VI. 1.3. --- Testosterone-Repressed Prostate Message-2 Gene --- p.20 / Chapter VI. 1.4. --- Other Programmed Cell Death Associated Gene Expressions --- p.21 / Chapter VI.2. --- Protooncogene Regulation in Programmed Cell Death --- p.22 / Chapter VI.2.1. --- bcl-2 Expression --- p.22 / Chapter VI.2.2. --- c-myc Expression --- p.23 / Chapter VII. --- Concanavalin A and succinylated Concanavalin A --- p.25 / Chapter VII. 1. --- Physiochemical Characterization --- p.25 / Chapter VII.2. --- Cellular Response to Concanavalin A --- p.29 / Chapter VIII. --- Features of Murine Macrophage Cell Line PU5-1.8 and Normal Macrophages --- p.32 / Chapter Section 2 --- Materials and Methods / Chapter I. --- Materials --- p.33 / Chapter II. --- Cell Culture --- p.33 / Chapter III. --- [Methyl-3H]-Thymidine Incorporation Assay --- p.34 / Chapter IV. --- [Methyl-3H]-Thymidine Release Assay --- p.34 / Chapter V. --- "3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT ) Cell Death Assay" --- p.35 / Chapter VI. --- Identification of Cell Death using DNA Chelating Fluorescence Probes´ؤFluorescent Microscopy and Confocal Laser Microscopy --- p.35 / Chapter VII. --- Analysis of DNA Fragmentation --- p.37 / Chapter VIII. --- Determination of Fluxes by Confocal Laser Microscopy --- p.38 / Chapter IX. --- Determination of PKC Activation by Western Blotting and Immunocytochemistry --- p.39 / Chapter X. --- Statistical Analysis --- p.41 / Chapter Section 3 --- Results / Chapter I. --- Concanavalin A was a Cell Death Causing Agent in PU5-1.8 cells --- p.42 / Chapter I.1 --- Con A Reduced the Cell Proliferation in PU5-1.8 cells --- p.42 / Chapter I.2. --- Con A Exhibited Cytotoxic Effect to PU5-1.8 cells --- p.44 / Chapter I.3. --- Con A Exhibited Cytotoxic Effect on Normal Peritoneal Macrophages --- p.46 / Chapter I.4. --- Succinylated Concanavalin A Showed a Weaker Cytotoxic Effect in the PU5-1.8 cells --- p.46 / Chapter I.5. --- α-D-Methylmannopyranoside Inhibited the Cytotoxic Effect of Con A in PU5-1.8 cells --- p.50 / Chapter I.6. --- FCS Inhibited the Con A-induced cell death of PU5-1.8 cells --- p.52 / Chapter II. --- Concanavalin A was an Apoptosis Causing Agentin PU5-1.8 cells --- p.57 / Chapter II. 1. --- Con A Induced Apoptosis in PU5-1.8 cells --- p.57 / Chapter II. 2. --- Con A Enhanced the Release of DNA in PU5-1.8 cell --- p.63 / Chapter II. 3. --- Con A Induced DNA fragmentation in PU5-1.8 cells --- p.63 / Chapter II.4. --- Cycloheximide Inhibited the Con A-Induced Cell Death in PU5-1.8 cells --- p.67 / Chapter II.5. --- Nicotinamide Inhibited the Con A-Induced Cell Death in PU5-1.8 cells --- p.71 / Chapter III. --- Signaling elicited by Concanavalin A --- p.74 / Chapter III.1. --- Con A Increased Intracellular Free Calcium Ion Concentration of PU5-1.8 cells --- p.74 / Chapter III. 1.1. --- Con A Induced Ca2+ Mobilization in PU5-1.8 cells --- p.74 / Chapter III. 1.2. --- Con A Induced the Ca2+ Influx and Intracellular Ca2+ Mobilization --- p.78 / Chapter III. 1.3. --- BAPTA-AM Inhibited the Ca2+ Mobilization in PU5-1.8 cells Stimulated by Con A --- p.80 / Chapter III.2. --- Role of Protein kinase C --- p.86 / Chapter III.2.1. --- Con A Increased the amount of PKC in PU5-1.8 cells --- p.86 / Chapter III.2.2. --- Con A translocated the Protein Kinase C from Cytosol into Subnuclear Region --- p.86 / Chapter III.2.3. --- The Cell Death Induced by Con A Is Partially Inhibited by PKC Depletion But not by Staurosporine --- p.89 / Chapter Section 4 --- Discussions / Chapter I. --- PU5-1.8 cells as a Model for the Study of Cell Deathin Macrophages --- p.94 / Chapter II. --- Concanavalin A caused Cell Death in PU5-1.8 cells --- p.95 / Chapter III. --- Concanavalin A induced Programmed Cell Death in PU5-1.8 cells --- p.97 / Chapter IV. --- Increase in Intracellular Calcium was not Required in Con A-induced Cell Death --- p.100 / Chapter V. --- Activation of Protein Kinase C was Partially Required for Con A-induced Cell Death --- p.101 / Chapter VI. --- General Discussions --- p.102 / Chapter Section 5 --- Bibliography --- p.104 / Reference --- p.104

Because of the measuring instrument or the biological variability, many studies with survival data involve covariates which are subject to measurement error. In such cases, the naive estimates are usually biased. In this thesis, we propose a bias corrected estimate of the regression parameter for the multinomial probit regression model with covariate measurement error. Our method handles the case when the response variable is subject to interval censoring, a frequent occurrence in many medical and health studies where patients are followed periodically. A sandwich estimator for the variance is also proposed. Our procedure can be generalized to general measurement error distribution as long as the first four moments of the measurement error are known. The results of extensive simulations show that our approach is very effective in eliminating the bias when the measurement error is not too large relative to the error term of the regression model. / Censoring is an intrinsic part in survival analysis. In this thesis, we establish the asymptotic properties of MMLE to general transformation models when data is subject to right or left censoring. We show that MMLE is not only consistent and asymptotically normal, but also asymptotically efficient. Thus our asymptotic results give a definite answer to a long-term argument on the efficiency of the maximum marginal likelihood estimator. The difficulty in establishing these results comes from the fact that the score function derived from the marginal likelihood does not have ordinary independence or martingale structure. We will develop a discretization method in establishing our results. As a special case, our results imply the consistency, asymptotic normality and efficiency for the multinomial probit regression, a popular alternative to the Cox regression model. / General transformation model is an important family of semiparametric models in survival analysis which generalizes the linear transformation model. It not only includes typical Cox regression model, proportional odds model and multinomial probit regression model, but also includes heteroscedastic hazard regression model, general heteroscedastic rank regression model and frailty model. By maximizing the marginal likelihood, a parameter estimation (MMLE) can be obtained with the property that it avoids estimating the baseline survival function and censoring distribution, and such property is enjoyed by the Cox regression model. In this thesis, we study three areas of generalization of general transformation models: main response variable is subject to censoring, covariates are time-varying and covariates are subject to measurement error. / In medical studies, the covariates are not always the same during the whole period of study. Covariates may change at certain time points. For example, at the beginning, n patients accept drug A as treatment. After certain percentage of patients have died, the investigator might add new drug B to the rest of the patients. This corresponds to the case of time-varying covariates. In this thesis, we propose an estimation procedure for the parameters in general transformation model with this type of time-varying covariates. The results of extensive simulations show that our approach works well. / Wu, Yueqin. / Adviser: Ming Gao Gu. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3589. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 74-78). / 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, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Tsui Yuen-Yee. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 53-56). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Parametric Regressions in Survival Analysis --- p.6 / Chapter 2.1 --- Introduction --- p.6 / Chapter 2.2 --- Exponential Regression --- p.7 / Chapter 2.3 --- Weibull Regression --- p.8 / Chapter 2.4 --- Maximum Likelihood Method --- p.9 / Chapter 2.5 --- Diagnostic --- p.10 / Chapter 3 --- Local Influence --- p.13 / Chapter 3.1 --- Introduction --- p.13 / Chapter 3.2 --- Development --- p.14 / Chapter 3.2.1 --- Normal Curvature --- p.14 / Chapter 3.2.2 --- Conformal Normal Curvature --- p.15 / Chapter 3.2.3 --- Q-displacement Function --- p.16 / Chapter 3.3 --- Perturbation Scheme --- p.17 / Chapter 4 --- Examples --- p.21 / Chapter 4.1 --- Halibut Data --- p.21 / Chapter 4.1.1 --- The Data --- p.22 / Chapter 4.1.2 --- Initial Analysis --- p.23 / Chapter 4.1.3 --- Perturbations of σ around 1 --- p.23 / Chapter 4.2 --- Diabetic Data --- p.30 / Chapter 4.2.1 --- The Data --- p.30 / Chapter 4.2.2 --- Initial Anaylsis --- p.31 / Chapter 4.2.3 --- Perturbations of σ around σ --- p.31 / Chapter 5 --- Conclusion Remarks and Further Research Topic --- p.35 / Appendix A --- p.38 / Appendix B --- p.47 / Bibliography --- p.53