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The purpose of this thesis is to identify and evaluate factors that affect retention and promotion of mid-grade officers in the U.S. Marine Corps. The analysis includes evaluation of survival patterns to ten-years of commissioned service and promotion patterns to O-4 and O-5. The primary goal is to explain the effect of an officersâ primary military occupational specialty (PMOS) on retention and promotion. The Marine Corps Commissioned Officer Accession Career (MCCOAC) data file contains cohort information from FY 1980 through FY 1999 and includes 27,659 observations. Using data from the MCCOAC data file, logistic regression and Cox Proportional Hazard models are used to estimate the effects of an officerâ s PMOS on survival and promotion patterns of Marine Corps officers. The findings indicate that an officers PMOS is significantly associated with whether an officer stays until 10 YCS or is promoted to O-4 or O-5. Logistic regression results show that pilot PMOSs are positively correlated with surviving until 10 YCS, but are negatively correlated with promotion to O-4, when compared to Infantry. The results also find that the remaining PMOSs are negatively correlated with whether and officer survives until 10 YCS, when compared to Infantry. In addition, only three PMOSs (0402, 7202, and 7523) are positively correlated with whether an officer is promoted to O-4 or O-5. Finally, the Cox Proportional Hazard results show the effect of having a particular PMOS or occupational field on the hazards of separation and promotion.

Master of Agribusiness / Department of Agricultural Economics / Bryan W. Schurle / The objective of this thesis is to develop a simulation model to determine the probability of achieving a market share goal. Two different simulation models were developed and compared allowing the author to select the best model. The first simulation model developed used the current market share as the mean and the standard deviation of historical market share as the standard deviation. So, a market share of 31.00% and a standard deviation of 3.88% were used in the simulation. When these values were simulated the results determined the probability of achieving the market share goal of 33%. The simulation results indicated that only 12 out of 100 observations resulted in market share greater than the goal. Therefore, there is a 12% probability of achieving or exceeding the market share goal based on the current market share and historical market share standard deviation. To predict future market share, a regression model was used to determine the impact of factors on market share. The regression model was used to forecast an estimate of market share. This forecasted share of 31.13% was used as the mean and 3.45%, the standard error of the model, was used to generate a second simulation model. The simulation results indicated that 26 of 100 observations resulted in market share greater than the goal of 33%. This indicates that there is a 26% probability of achieving or exceeding the market share goal based on results using regression to predict future market share and variability in market share. The second simulation model generated from the market share forecast and standard error from the regression model produced the better results. When using a regression model, it resulted in a higher estimate for meeting the goal. The addition of independent variables that impact share explained more of the variability around the projected mean than the historical model did.

The class of beta regression models is commonly used by practitioners to model variables that assume values in the standard unit interval (0, 1). It is based on the assumption that the dependent variable is beta-distributed and that its mean is related to a set of regressors through a linear predictor with unknown coefficients and a link function. The model also includes a precision parameter which may be constant or depend on a (potentially different) set of regressors through a link function as well. This approach naturally incorporates features such as heteroskedasticity or skewness which are commonly observed in data taking values in the standard unit interval, such as rates or proportions. This paper describes the betareg package which provides the class of beta regressions in the R system for statistical computing. The underlying theory is briefly outlined, the implementation discussed and illustrated in various replication exercises. / Series: Research Report Series / Department of Statistics and Mathematics

Ka Cheuk Yin Timothy. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 52-54). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Stress Test --- p.1 / Chapter 1.2 --- Extreme Value Regression --- p.2 / Chapter 1.3 --- Type II Censoring --- p.4 / Chapter 1.4 --- Test Plan --- p.5 / Chapter 1.5 --- The Scope of the Thesis --- p.6 / Chapter 2 --- Extreme Value Regression Model --- p.7 / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Maximum Likelihood Estimation --- p.8 / Chapter 2.3 --- Variance-Covariance Matrix --- p.9 / Chapter 3 --- Optimality Criteria and Allocation Methods --- p.15 / Chapter 3.1 --- Introduction --- p.15 / Chapter 3.2 --- Optimality Criteria --- p.16 / Chapter 3.3 --- Allocation Methods --- p.17 / Chapter 4 --- Asymptotic Results --- p.21 / Chapter 4.1 --- Introduction --- p.21 / Chapter 4.2 --- Asymptotic Variance-Covariance Matrix --- p.22 / Chapter 4.3 --- Optimality Criteria --- p.29 / Chapter 5 --- Optimal Allocations --- p.32 / Chapter 5.1 --- Introduction --- p.32 / Chapter 5.2 --- Allocation for small sample size --- p.33 / Chapter 5.2.1 --- 2-stress-level case --- p.33 / Chapter 5.2.2 --- 4-stress-level case --- p.34 / Chapter 5.2.3 --- Suggested Optimal Allocation --- p.39 / Chapter 5.2.4 --- Comparison with the complete sample case --- p.43 / Chapter 5.3 --- Asymptotic Allocations --- p.44 / Chapter 6 --- Conclusions and Further Research --- p.50 / Bibliography --- p.52

In this dissertation, we develop regression models for estimating interactions between a treatment variable and a set of baseline predictors in their eect on the outcome in a randomized trial, without restriction to a linear relationship. The proposed semiparametric/nonparametric regression approaches for representing interactions generalize the notion of an interaction between a categorical treatment variable and a set of predictors on the outcome, from a linear model context. In Chapter 2, we develop a model for determining a composite predictor from a set of baseline predictors that can have a nonlinear interaction with the treatment indicator, implying that the treatment efficacy can vary across values of such a predictor without a linearity restriction. We introduce a parsimonious generalization of the single-index models that targets the eect of the interaction between the treatment conditions and the vector of predictors on the outcome. A common approach to interrogate such treatment-by-predictor interaction is to t a regression curve as a function of the predictors separately for each treatment group. For parsimony and insight, we propose a single-index model with multiple-links that estimates a single linear combination of the predictors (i.e., a single-index), with treatment-specic nonparametrically-dened link functions. The approach emphasizes a focus on the treatment-by-predictors interaction eects on the treatment outcome that are relevant for making optimal treatment decisions. Asymptotic results for estimator are obtained under possible model misspecication. A treatment decision rule based on the derived single-index is dened, and it is compared to other methods for estimating optimal treatment decision rules. An application to a clinical trial for the treatment of depression is presented to illustrate the proposed approach for deriving treatment decision rules. In Chapter 3, we allow the proposed single-index model with multiple-links to have an unspecified main effect of the predictors on the outcome. This extension greatly increases the utility of the proposed regression approach for estimating the treatment-by-predictors interactions. By obviating the need to model the main eect, the proposed method extends the modied covariate approach of [Tian et al., 2014] into a semiparametric regression framework. Also, the approach extends [Tian et al., 2014] into general K treatment arms. In Chapter 4, we introduce a regularization method to deal with the potential high dimensionality of the predictor space and to simultaneously select relevant treatment effect modiers exhibiting possibly nonlinear associations with the outcome. We present a set of extensive simulations to illustrate the performance of the treatment decision rules estimated from the proposed method. An application to a clinical trial for the treatment of depression is presented to illustrate the proposed approach for deriving treatment decision rules. In Chapter 5, we develop a novel additive regression model for estimating interactions between a treatment and a potentially large number of functional/scalar predictor. If the main effect of baseline predictors is misspecied or high-dimensional (or, innite dimensional), any standard nonparametric or semiparametric approach for estimating the treatment-bypredictors interactions tends to be not satisfactory because it is prone to (possibly severe) inconsistency and poor approximation to the true treatment-by-predictors interaction effect. To deal with this problem, we impose a constraint on the model space, giving the orthogonality between the main and the interaction effects. This modeling method is particularly appealing in the functional regression context, since a functional predictor, due to its infinite dimensional nature, must go through some sort of dimension reduction, which essentially involves a main effect model misspecication. The main effect and the interaction effect can be estimated separately due to the orthogonality between the two effects, which side-steps the issue of misspecication of the main effect. The proposed approach extends the modied covariate approach of [Tian et al., 2014] into an additive regression model framework. We impose a concave penalty in estimation, and the method simultaneously selects functional/scalar treatment effect modifiers that exhibit possibly nonlinear interaction effects with the treatment indicator. The dissertation concludes in Chapter 6.

一個社會經濟學的目標變量，經常存在兩種不同收集頻率的數據。由於較低頻率的一組數據通常由大型普查中所獲得，其準確度及可靠性會較高。因此較低頻率的一組數據一般會視作基準，用作對頻率較高的另一組數據進行修正。 / 在基準修正過程中，一般會假設調查誤差及目標數據的大小互相獨立，即「累加模型」。然而，現實中兩者通常是相關的，目標變量越大，調查誤差亦會越大，即「乘積模型」。對此問題，陳兆國及胡家浩提出了利用準線性回歸手法對乘積模型進行基準修正。在本論文中，假設調查誤差服從AR(1)模型，首先我們會示範如何利用準線性回歸手法及默認調查誤差模型進行基準數據修正。然後，運用基準預測的方式，提出一個對調查誤差模型的估計辦法。最後我們會比較兩者的表現以及一些選擇誤差模型的指引。 / For a target socio-economic variable, two sources of data with different collecting frequencies may be available in survey data analysis. In general, due to the difference of sample size or the data source, two sets of data do not agree with each other. Usually, the more frequent observations are less reliable, and the less frequent observations are much more accurate. In benchmarking problem, the less frequent observations can be treated as benchmarks, and will be used to adjust the higher frequent data. / In the common benchmarking setting, the survey error and the target variable are always assumed to be independent (Additive case). However, in reality, they should be correlated (Multiplicative case). The larger the variable, the larger the survey error. To deal with this problem, Chen and Wu (2006) proposed a regression method called quasi-linear regression for the multiplicative case. In this paper, by assuming the survey error to be an AR(1) model, we will demonstrate the benchmarking procedure using default error model for the quasi-linear regression. Also an error modelling procedure using benchmark forecast method will be proposed. Finally, we will compare the performance of the default error model with the fitted error model. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Luk, Wing Pan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 56-57). / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Recent Development For Benchmarking Methods --- p.2 / Chapter 1.2 --- Multiplicative Case And Benchmarking Problem --- p.3 / Chapter 2 --- Benchmarking With Quasi-linear Regression --- p.8 / Chapter 2.1 --- Iterative Procedure For Quasi-linear Regression --- p.9 / Chapter 2.2 --- Prediction Using Default Value φ --- p.16 / Chapter 2.3 --- Performance Of Using Default Error Model --- p.17 / Chapter 3 --- Estimation Of φ Via BM Forecasting method --- p.26 / Chapter 3.1 --- Benchmark Forecasting Method --- p.26 / Chapter 3.2 --- Performance Of Benchmark Forecasting Method --- p.28 / Chapter 4 --- Benchmarking By The Estimated Value --- p.34 / Chapter 4.1 --- Benchmarking With The Estimated Error Model --- p.35 / Chapter 4.2 --- Performance Of Using Estimated Error Model --- p.36 / Chapter 4.3 --- Suggestions For Selecting Error Model --- p.45 / Chapter 5 --- Fitting AR(1) Model For Non-AR(1) Error --- p.47 / Chapter 5.1 --- Settings For Non-AR(1) Model --- p.47 / Chapter 5.2 --- Simulation Studies --- p.48 / Chapter 6 --- An Illustrative Example: The Canada Total Retail Trade Se-ries --- p.50 / Chapter 7 --- Conclusion --- p.54 / Bibliography --- p.56

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

The purpose of this thesis is to run a panel regression analyzing the impact of economic, legislative and social factors on corporate tax revenues, as defined by existing empiric and theoretic literature. Literature which directly addresses factors influencing corporate income tax revenues is quite limited -- in respect of number of papers as well as in respect of the range of examined countries and/or time period. The latest and key papers include among others Clausing (2007), Devereux (2006) and partly Kenny, Winer (2006) and Gropp, Kostial (2000). Presented thesis on the other hand covers observations for all OECD countries for a rather long time period 1980 -- 2006. This thesis should address all important factors having influence on corporate income tax revenues, including tax avoidance and debt financing. The results of the analysis largely correspond to existing investigations of other authors; however, presented regression is of more complex and general character -- it includes other factors of tax avoidance and data for all OECD members (except for some variables which are not available), including post-communist countries.