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

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

Approved for public release; distribution is unlimited / This study explores whether the attacker's daily casualty rate (DCR) changes according to the terrain. The data set is a part of a larger database, Division Level Engagement Database from the Dupuy Institute. There are data on 253 battles, 96 of which occurred in urban areas. All the engagements are selected from European Theater of Operation (ETO) in World War II. The available data set contains measurements about the battles like initial strengths, daily casualties, terrain, front width, linear density, attacker's and defender's country, and armor losses. Hypothesis tests are used to find if the DCR is different in urban operations. A linear regression model is constructed to predict outcomes of similar engagements and to see the effect of each variable. It is concluded that the attacker's daily casualty rate is, on average, lower in urban operations. Terrain and force ratio are the most effective drivers of the daily casualty rate. In addition, it is seen that allied forces (U.S., U.K. and Canada) had a different approach to Military Operations on Urban Terrain than Soviet and German forces. The Allies used extensive combat power in urban operations. / First Lieutenant, Turkish Army

is shown to follow an exponential distribution. These three models enable operators to explore the impact of various inputs prior to programming the vehicle, thus allowing them to choose the best combination of vehicle parameters that minimize the offset error between the reported and actual locations.

Approved for public release, distribution is unlimited / This thesis compares characteristics that influence intended stay/leave behavior of non prior service junior naval officers from two different time periods. Samples of officers, under the rank of lieutenant, with less then six years of active duty service from the 1992 and 1999 Department of Defense Surveys of Active Duty Personnel were used for this analysis. Metrics for important determinates of retention were constructed using similar questions from both surveys. Logistic regression was used to identify significant influences on retention intentions in both survey years. Two composite dimensions positively affected retention intentions in both survey years: satisfaction with Service Attributes and satisfaction with Present Employment Attributes. Being female negatively affected retention intentions in both surveys. The minority variable, Black, the number of PCS moves, and having debt greater than $7 ,500 positively affected retention intentions, while being stationed onboard a ship, probability of finding a civilian job, and the composite dimension, satisfaction with Future Employment Attributes, negatively affected retention intentions in 1992. Influence from a significant other and the number of hours worked positively affected retention in 1999. Monetary variables were highly significant for retention intentions in 1992 but not in 1999. It is likely that the force drawdown, base closures, and a weak economy in 1992 explain these differences. / Lieutenant, United States Navy

Researchers who make predictions from educational data are interested in choosing the best regression model possible. Many criteria have been devised for choosing a full or restricted model, and also for selecting the best subset from an all-possible-subsets regression. The relative practical usefulness of three of the criteria used in selecting a regression model was compared in this study: (a) Mallows' C_p, (b) Amemiya's prediction criterion, and (c) Hagerty and Srinivasan's method involving predictive power. Target correlation matrices with 10,000 cases were simulated so that the matrices had varying degrees of effect sizes. The amount of power for each matrix was calculated after one or two predictors was dropped from the full regression model, for sample sizes ranging from n = 25 to n = 150. Also, the null case, when one predictor was uncorrelated with the other predictors, was considered. In addition, comparisons for regression models selected using C_p and prediction criterion were performed using data from the National Educational Longitudinal Study of 1988.

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

by Koon-Sun Wong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 40-41). / Chapter Chapter 1 --- Introduction and Review --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- Preliminaries --- p.4 / Chapter 1.3 --- A brief review of A-optimal designs --- p.8 / Chapter Chapter 2 --- D-optimal Designs --- p.13 / Chapter 2.1 --- A D-optimal design problem --- p.13 / Chapter 2.2 --- A theorem for D-optimal designs --- p.14 / Chapter Chapter 3 --- E-optimal Designs --- p.18 / Chapter 3.1 --- An E-optimal design problem --- p.18 / Chapter 3.2 --- A theorem for E-optimal designs --- p.19 / Chapter Chapter 4 --- An alternative method for computing CL vectors --- p.31 / Chapter Chapter 5 --- Conclusions --- p.36 / References --- p.40

Chan Wing-Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 43-45). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Estimation of Volatility --- p.5 / Chapter 2.1 --- A revisit to the RiskMetrics --- p.6 / Chapter 2.2 --- Predicting Multiple-period of Volatilities --- p.7 / Chapter 2.3 --- Performance Measures --- p.11 / Chapter 2.4 --- Nonparametric Estimation of Quantiles --- p.13 / Chapter 3 --- Univariate Prediction --- p.15 / Chapter 3.1 --- Piecewise Constant Technique --- p.16 / Chapter 3.2 --- Piecewise Linear Technique --- p.22 / Chapter 4 --- Bivariate Prediction --- p.27 / Chapter 4.1 --- Model Selection --- p.28 / Chapter 4.2 --- Piecewise Linear with Discontinuity --- p.29 / Chapter 4.3 --- Piecewise Linear Technique --- p.35 / Chapter 5 --- Conclusions --- p.41 / Bibliography --- p.43

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