Typescript. / Digitized by Kansas Correctional Industries

Thesis. 1975. Ph.D.--Massachusetts Institute of Technology. Dept. of Economics. / Vita. / Bibliography: leaves 213-214. / Ph.D.

The need for formative assessments has led to the development of a psychometric framework known as diagnostic classification models (DCMs), which are mathematical measurement models designed to estimate the possession or mastery of a designated set of skills or attributes within a chosen construct. Furthermore, much research has gone into the practice of “retrofitting” diagnostic measurement models to existing assessments in order to improve their diagnostic capability. Although retrofitting DCMs to existing assessments can theoretically improve diagnostic potential, it is also prone to challenges including identifying multidimensional traits from largely unidimensional assessments, a lack of assessments that are suitable for the DCM framework, and statistical quality, specifically highly correlated attributes and poor model fit. Another recent trend in assessment has been a move towards creating more authentic constructed-response assessments. For such assessments, rubric-based scoring is often seen as method of providing reliable scoring and interpretive formative feedback. However, rubric-scored tests are limited in their diagnostic potential in that they are usually used to assign unidimensional numeric scores. It is the purpose of this thesis to propose general methods for retrofitting DCMs to rubric-scored assessments. Two methods will be proposed and compared: (1) automatic construction of an attribute hierarchy to represent all possible numeric score levels from a rubric-scored assessment and (2) using rubric criterion score level descriptions to imply an attribute hierarchy. This dissertation will describe these methods, discuss the technical and mathematical issues that arise in using them, and apply and compare both methods to a prominent rubric-scored test of critical thinking skills, the Collegiate Learning Assessment+ (CLA+). Finally, the utility of the proposed methods will be compared to a reasonable alternative methodology: the use of polytomous IRT models, including the Graded Response Model (GRM), the Partial Credit Model (PCM), and the Generalized-Partial Credit Model (G-PCM), for this type of test score data.

The model equations for storm surge and tsunamis most commonly used are the shallow water equations with addition of appropriate source terms for bathymetry. Traditional approaches will need to resolve the mesh to discretize small-scale structure, which impacts the time-step size to be proportional to the size of cells. In this thesis, a novel approximate Riemann solver was developed in order to deal with the existence of barrier without restricting the time-step due to small cells. Because of the wave redistribution method and proper ghost cells setting, the novel Riemann solver maintained properties including mass and momentum conservation, the well-balancing properties and robustness at the wet-dry interface. The solver also preserves nonnegative water depth and prevents leakage. A modified h-box method is applied so the algorithm can overcome restrictions of small time-step sizes. The work has been done in the context of the GeoClaw platform with retaining the capabilities of GeoClaw solver. At the same time, the special developed Riemann solver extends the package to handle the sub-grid-scale effects of barriers. Incorporating the solver developed in this work into the GeoClaw framework has allowed to leverage GeoClaw’s ability to handle complex bathymetry and real applications.

Man Po Kong = 信貸相聯票據和住宅按揭的預期損失之估值 / 文普綱. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 85-86). / Text in English; abstracts in English and Chinese. / Man Po Kong = Xin dai xiang lian piao ju he zhu zhai an jie de yu qi sun shi zhi gu zhi / Wen Pugang. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- The Structural model --- p.3 / Chapter 2.1 --- Merton's model --- p.3 / Chapter 2.2 --- The term structure of interest rate --- p.7 / Chapter 2.3 --- The default-triggering mechanism and derivations from strict priority rule --- p.9 / Chapter 2.4 --- Stationary leverage ratio --- p.11 / Chapter 2.5 --- The three-factor structural model --- p.12 / Chapter 3 --- Credit-linked Notes with early default risk --- p.18 / Chapter 3.1 --- Introduction to credit-linked notes --- p.18 / Chapter 3.2 --- The pricing of credit-linked notes --- p.20 / Chapter 3.3 --- Non mean-reverting leverage ratios --- p.21 / Chapter 3.3.1 --- Special case (pQv=0) --- p.23 / Chapter 3.4 --- Mean reverting leverage ratios --- p.25 / Chapter 4 --- Numerical results and discussion --- p.28 / Chapter 4.1 --- Exact solution (KQ=kv=PQv=PVr=0) --- p.31 / Chapter 4.2 --- "Lower bound approximation (kQ,kv≠0,pQr,pvr≠0)" --- p.37 / Chapter 4.2.1 --- Effect of interest rate --- p.43 / Chapter 4.3 --- Monte Carlo simulation (PQV≠0) --- p.47 / Chapter 5 --- Expected loss of residential mortgage loans --- p.56 / Chapter 5.1 --- Introduction to residential mortgage loans --- p.56 / Chapter 5.2 --- Calculation of expected loss of residential mortgage loans --- p.59 / Chapter 6 --- Numerical results and discussion --- p.65 / Chapter 6.1 --- Numerical results --- p.65 / Chapter 7 --- Conclusion --- p.73 / Chapter A --- Methodology --- p.75 / Chapter A.1 --- Monte Carlo Simulation --- p.76 / Chapter A.2 --- Finding lower and upper bound approach --- p.79 / Chapter A.2.1 --- Single stage approximation --- p.79 / Chapter A.2.2 --- Multistage lower bound approximation --- p.82 / Bibliography --- p.85

Choy Man Wah Minnie. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 83). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- The Analysis of APT using SEM --- p.3 / Chapter 2.1 --- The APT model --- p.3 / Chapter 2.2 --- The structural equation model approach --- p.5 / Chapter 3 --- Incorporating stochastic constraints into the SEM analysis of APT --- p.8 / Chapter 3.1 --- Introduction --- p.8 / Chapter 3.2 --- Bayesian analysis of stochastic constraints --- p.9 / Chapter 3.3 --- Three types of structures for T I --- p.10 / Chapter 3.3.1 --- Case 1: T = (σ2Imxm --- p.10 / Chapter 3.3.2 --- "Case 2: r is a diagonal matrix with diagonal elements σ2j for = 1， …,m" --- p.13 / Chapter 3.3.3 --- Case 3: Γ is a general positive definite matrix --- p.14 / Chapter 3.4 --- Estimation of parameters using the Mx program --- p.16 / Chapter 4 --- Empirical study on Hong Kong stock market --- p.17 / Chapter 4.1 --- Information of data --- p.17 / Chapter 4.2 --- Source of data --- p.17 / Chapter 4.3 --- Lisrel model with exact constraints --- p.19 / Chapter 4.3.1 --- The resultant model --- p.20 / Chapter 4.4 --- Lisrel model with stochastic constraints --- p.21 / Chapter 4.4.1 --- Result --- p.22 / Chapter 5 --- Simulation study --- p.35 / Chapter 5.1 --- Simulation design --- p.35 / Chapter 5.2 --- Simulation procedure --- p.40 / Chapter 5.3 --- Simulation result --- p.41 / Chapter 5.3.1 --- Sample size --- p.41 / Chapter 5.3.2 --- Analysis methods (constraints) --- p.42 / Chapter 5.3.3 --- Factor loadings --- p.43 / Chapter 5.3.4 --- Factor correlation matrix --- p.43 / Chapter 5.3.5 --- Risk premia --- p.43 / Chapter 5.3.6 --- Overall result --- p.44 / Chapter 6 --- Conclusion and discussion --- p.45 / Appendices --- p.46 / Chapter A --- Simulation result - Mean --- p.47 / Chapter B --- Simulation result - Bias --- p.56 / Chapter C --- Simulation result - RMSE --- p.65 / Chapter D --- Mx input script --- p.74 / Chapter D.l --- Stochastic constraints Case 1 --- p.74 / Chapter D.2 --- Stochastic constraints Case 2 --- p.77 / Chapter D.3 --- Stochastic constraints Case 3 --- p.80 / Bibliography --- p.83

Yeung Kin Bong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 69-73). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Credit risk modeling --- p.3 / Chapter 1.2 --- Uniqueness of bi-directional: hybrid system --- p.4 / Chapter 1.3 --- Scope of the study --- p.5 / Chapter 2 --- Literature Review --- p.6 / Chapter 2.1 --- Statistical / Empirical approach --- p.6 / Chapter 2.2 --- Structural approach --- p.8 / Chapter 3 --- Background --- p.10 / Chapter 3.1 --- Merton structural default model --- p.10 / Chapter 3.2 --- Cross-sectional regression analysis (CRA) --- p.15 / Chapter 3.3 --- Neural network learning (NN) --- p.16 / Chapter 3.3.1 --- Single-layer network --- p.17 / Chapter 3.3.2 --- Multi-layer perceptron (MLP) --- p.20 / Chapter 3.3.3 --- Back-propagation network --- p.22 / Chapter 3.3.4 --- "Supervised, unsupervised and combine unsupervised-supervised learning" --- p.23 / Chapter 3.4 --- Weaknesses of uni-directional modeling --- p.23 / Chapter 4 --- Methodology --- p.26 / Chapter 4.1 --- Bi-directional modeling --- p.26 / Chapter 4.2 --- Asset price estimation --- p.31 / Chapter 4.3 --- Quantifying accounting data noise --- p.33 / Chapter 5 --- Proposed Model --- p.37 / Chapter 5.1 --- Core of the model --- p.37 / Chapter 5.2 --- Feature selection --- p.41 / Chapter 5.3 --- Bi-directional default neural system --- p.44 / Chapter 6 --- Implementations --- p.49 / Chapter 6.1 --- Data preparation --- p.50 / Chapter 6.2 --- Experiment --- p.51 / Chapter 6.3 --- Empirical results --- p.61 / Chapter 6.3.1 --- Predicted spreads from the uni-directional models --- p.61 / Chapter 6.3.2 --- Predicted spreads from the proposed bi-directional model --- p.63 / Chapter 6.3.3 --- Performance comparison --- p.64 / Chapter 7 --- Conclusions --- p.67 / Bibliography --- p.69

Yick, Kwok Leung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 67-68). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The importance of forecasting --- p.1 / Chapter 1.2 --- Objective --- p.3 / Chapter 2 --- Vector Autoregressive Model --- p.5 / Chapter 2.1 --- The VAR(p) model --- p.5 / Chapter 2.2 --- Least squares estimation method --- p.7 / Chapter 2.3 --- VAR order selection method --- p.10 / Chapter 2.4 --- Constructing simultaneous prediction intervals procedures --- p.11 / Chapter 2.4.1 --- Bonferroni procedure --- p.12 / Chapter 2.4.2 --- The 'Exact' procedure --- p.13 / Chapter 2.4.3 --- Two variables case --- p.15 / Chapter 2.4.4 --- Three variables case --- p.18 / Chapter 3 --- A System of Linear Equations with Exogenous Variables --- p.23 / Chapter 3.1 --- Restriction of VAR model --- p.23 / Chapter 3.2 --- Least squares estimation method --- p.24 / Chapter 3.3 --- Hsiao's sequential method for estimating the lag lengths --- p.26 / Chapter 3.3.1 --- Two variables case --- p.27 / Chapter 3.3.2 --- Three variables case --- p.29 / Chapter 3.4 --- Using VAR model to construct simultaneous prediction intervals --- p.32 / Chapter 3.4.1 --- Bonferroni procedure --- p.34 / Chapter 3.4.2 --- The 'Exact' procedure --- p.35 / Chapter 3.4.3 --- Two variables case --- p.36 / Chapter 3.4.4 --- Three variables case --- p.38 / Chapter 4 --- Illustrative Examples --- p.42 / Chapter 5 --- A Simulation Study --- p.52 / Chapter 5.1 --- Design of the experiment --- p.52 / Chapter 5.2 --- Simulation results --- p.58 / Chapter 5.3 --- Concluding remarks --- p.60 / Chapter 5.4 --- Further research --- p.60 / References --- p.67

本論文的第一部分介紹一個帶基準約束的連續時間均值方差資產組合選擇問題。這個非凸優化問題將採用拉格朗日乘數來解決，並求出相應的答案及其存在準則。為了進行敏感性分析，相應的最佳投資組合及其一些導數將被明確求出。在第二部分中，我們採用標準的線性回歸技巧來檢定三個日曆效應是否在統計上顯著。其中最顯著的效應是四月及十二月的回報比全年平均為高。 / The first part of this thesis presents a benchmarked continuous-time mean-variance portfolio selection problem. The method of Lagrange multipliers is employed to solve this non-convex optimization problem, and the criterion for the existence of solution is derived accordingly. The corresponding efficient portfolio and its derivatives are explicitly derived for sensitivity analysis. The second part we employ the standard linear regression technique to test whether three calendar effects are statistically significant. The most significant effect is that the returns in April and December are higher than the average in the whole year. / Detailed summary in vernacular field only. / Yip, Fai Lung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 49-53). / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Mean Variance --- p.5 / Chapter 2.1 --- Model --- p.5 / Chapter 2.2 --- Portfolio Selection and the Solution --- p.9 / Chapter 2.3 --- Existence and Uniqueness of Lagrange Multipliers --- p.21 / Chapter 2.4 --- Optimal Trading Strategy --- p.29 / Chapter 2.5 --- Sensitivity Analysis --- p.34 / Chapter 3 --- Calendar Effect --- p.39 / Chapter 3.1 --- Data and Method --- p.39 / Chapter 3.2 --- Results --- p.42 / Chapter 4 --- Appendix --- p.47 / Chapter 4.1 --- Procedures Used to Obtain the Results in Chapter 4 --- p.47 / Bibliography --- p.49

門限協整是金融和統計研究中一個充滿活力的課題。其估計方法往往基於向量誤差修正模型，并儘限於單門限情形。本論文研究了多門限協整模型的估計問題。針對多門限協整，我們提出了兩種基於多門限向量誤差修正模型的估計方法：最小二乘估計和光滑最小二乘估計，并給出了最小二乘估計的收斂速度和建立了光滑最小二乘估計的極限分佈。爲了對這兩種估計方法的性能進行評估，我們展開了一項模擬實驗，實驗結果印證了本文給出的極限理論。通過多門限協整模型，我們隊利率期限結構進行了研究。 / 最後，本論文研究了光滑轉移協整的最小二乘估計方法，并給出了其極限分佈。 / Threshold cointegration has been a vibrant research topic in finance and statistics. Estimation procedures of threshold cointegrated models are usually based on the so-called threshold vector error correction forms (TVECMs) for one threshold case. In this thesis, we investigate two estimators for multiple thresholds cointegrations via TVECMs, namely the least squares estimator and the smoothed least squares estimator. The convergence rate of the least squares estimator is obtained and limiting distribution of the smoothed least squares estimator is developed. To assess the performance of these two estimators, we conduct a simulation study, the result of which supports the asymptotic theories developed. We study the term structure of interest rates by a two thresholds cointegration as an example. / Finally we also investigate the least squares estimator of smooth transition cointegration and establish the limiting distribution. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Wang, Man. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 83-88). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.1.1 --- Two-step Estimator --- p.3 / Chapter 1.1.2 --- Simultaneous Estimator --- p.4 / Chapter 1.2 --- Outline --- p.6 / Chapter 2 --- Threshold Cointegration --- p.7 / Chapter 2.1 --- Linear Cointegration --- p.7 / Chapter 2.1.1 --- Representation --- p.9 / Chapter 2.1.2 --- Two-step Estimator --- p.10 / Chapter 2.2 --- Threshold Cointegration --- p.12 / Chapter 2.2.1 --- SETAR Representation and Estimation --- p.12 / Chapter 2.2.2 --- TVECM Representation and Estimation --- p.15 / Chapter 3 --- LSE of Multipe Thresholds Cointegration --- p.17 / Chapter 3.1 --- Multipe Thresholds Cointegration --- p.17 / Chapter 3.2 --- TVECM Representation and LSE --- p.18 / Chapter 3.3 --- Assumptions and Results --- p.20 / Chapter 4 --- SLSE of Multiple Thresholds Cointegration --- p.25 / Chapter 4.1 --- Smoothed LSE (SLSE) --- p.25 / Chapter 4.2 --- TVECM and Estimation --- p.27 / Chapter 4.3 --- Assumptions and Results --- p.29 / Chapter 4.4 --- Asymptotic Variance --- p.34 / Chapter 5 --- Simulation and Empirical Studies --- p.38 / Chapter 5.1 --- Simulation Study --- p.38 / Chapter 5.1.1 --- Experiment Design --- p.38 / Chapter 5.1.2 --- Simulation Results --- p.40 / Chapter 5.2 --- Term Structure of Interest Rates --- p.42 / Chapter 6 --- Smooth Transition Cointegration --- p.50 / Chapter 6.1 --- Smooth Transition Cointegration --- p.51 / Chapter 6.2 --- Assumptions and Results --- p.52 / Chapter 7 --- Conclusion and Further Research --- p.56 / Chapter 7.1 --- Conclusion --- p.56 / Chapter 7.2 --- Future Research --- p.59 / Chapter 7.2.1 --- Nested Testing --- p.59 / Chapter 7.2.2 --- Limiting Distribution of LSE --- p.60 / Chapter 7.2.3 --- Other Nonlinear Cointegration --- p.60 / Chapter A --- Technical Proofs --- p.63 / Chapter B --- Some Formulas --- p.82 / Bibliography --- p.83