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Work zone tends to cause hazardous conditions for drivers and construction workers since work zones generate conflicts between construction activities and the traffic, therefore aggravate the existing traffic conditions and result in severe traffic safety and operational problems. To address the influence of various factors on the crash severity is beneficial to understand the characteristics of work zone crashes. The understanding can be used to select proper countermeasures for reducing the crash severity at work zones and improving work zone safety. In this dissertation, crash severity models were developed to explore the factor impacts on crash severity for two work zone crash datasets (overall crashes and rear-end crashes). Partial proportional odds logistic regression, which has less restriction to the parallel regression assumption and provides more reasonable interpretations of the coefficients, was used to estimate the models. The factor impacts were summarized to indicate which factors are more likely to increase work zone crash severity or which factors tends to reduce the severity. Because the speed variety is an important factor causing accidents at work zone area, the work zone speed profile was analyzed and modeled to predict the distribution of speed along the distance to the starting point of lane closures. A new learning machine algorithm, support vector regression (SVR), was utilized to develop the speed profile model for freeway work zone sections under various scenarios since its excellent generalization ability. A simulation-based experiment was designed for producing the speed data (output data) and scenario data (input data). Based on these data, the speed profile model was trained and validated. The speed profile model can be used as a reference for designing appropriate traffic control countermeasures to improve the work zone safety.

<p>Given the question of how to further decrease childhood mortality and attain the fourth MDG in Bangladesh, the determinants of childhood mortality and successful health intervention programmes in a rural area of Bangladesh are examined in this paper. The binominal logit regression analysis, on Matlab HDSS data from 2001 to 2005, indicates that the child’s birth order, outcome of mother’s previous pregnancy, mother’s age, mother’s education, economic condition of the household, immunization, and place of delivery are important determining factors of childhood mortality. Interview discussions show that the delivery of health services is a determining factor for successful health intervention programmes. It is worth to note that childhood mortality levels are no longer significantly lower in the treatment area of Matlab. Furthermore, the intervention programmes in the area require continuous reform, especially in the fields of birth assistance and injury prevention.</p>

Given the question of how to further decrease childhood mortality and attain the fourth MDG in Bangladesh, the determinants of childhood mortality and successful health intervention programmes in a rural area of Bangladesh are examined in this paper. The binominal logit regression analysis, on Matlab HDSS data from 2001 to 2005, indicates that the child’s birth order, outcome of mother’s previous pregnancy, mother’s age, mother’s education, economic condition of the household, immunization, and place of delivery are important determining factors of childhood mortality. Interview discussions show that the delivery of health services is a determining factor for successful health intervention programmes. It is worth to note that childhood mortality levels are no longer significantly lower in the treatment area of Matlab. Furthermore, the intervention programmes in the area require continuous reform, especially in the fields of birth assistance and injury prevention.

The challenges posed by climate change are a threat to human well-being as well as to natural ecosystems, but researchers indicate that awareness of and concern about climate change varies considerably. In this thesis, we investigate the socio-demographic factors associated with the perception of climate change, analysing four data sets collected from four surveys on urban middle-income households in Mexico. Our empirical strategy relies on the estimation of logit regressions on a binary variable defining whether respondents consider climate change as an important issue. Results indicate that age, gender, education, employment status, household size, and having in the household a member with a respiratory illness are important predictors that shape Mexicans’ perception of how important climate change is. It is important to know the determinants that effect climate change perception in order to develop sustainable policies to mitigate the risks of climate change.

The present dissertation research has three purposes: the first one is to predict anthropogenic deforestation caused by small farmers firstly using only pathways of past land cover change and secondly using demographic, socioeconomic and land cover data at the farm level. The second purpose is to compare the explanatory and predictive capacity of both approaches at identifying areas at high risk of deforestation among small farms in Rondõnia, Brazil. The third purpose is to test the assumptions of stationary probabilities and homogeneous subjects, both commonly used assumptions in predictive stochastic models applied to small farmers' deforestation decisions. This study uses the following data: household surveys, maps, satellite images and their land cover classification at the pixel level, and pathways of past land cover change for each farm. These data are available for a panel sample of farms in three municipios in Rondõnia, Brazil (Alto Paraiso, Nova União, and Rolim de Moura) and cover a ten-year period of study (1992-2002). Pathways of past land cover change are graphic representations in the form of flow charts that depict Land Cover Change (LCC) in each farm during the ten-year period of study. Pathways were constructed using satellite images, survey data and maps, and a set of interviews performed on a sub-sample of 70 farms. A panel data analysis of the estimated empirical probabilities was conducted to test for subject and time effects using a Fixed Group Effects Model (FGEM), specifically the Least Square Dummy Variable (LSDV1) fixed effects technique. Finally, the two predictive modeling approaches are compared. The first modeling approach predicts future LCC using only past land cover change data in the form of empirical transitional probabilities of LCC obtained from pathways of past LCC. These empirical probabilities are used in a LSDV1 for fixed–group effects, a LSDV1 for fixed-time effects, and an Ordinary Least Square model (OLS) for the pooled sample. Results from these models are entered in a modified Markov chain model's matrix multiplication. The second modeling approach predicts future LCC using socio-demographic and economic survey variables at the household level. The survey data is used to perform a multinomial logit regression model to predict the LC class of each pixel. In order to compare the explanatory and predictive capacity of both modeling approaches, LCC predictions at the pixel level are summarized in terms of percentage of cells in which future LC was predicted correctly. Percentage of correct predicted land cover class is compared against actual pixel classification from satellite images. The presence of differences among farmers in the LSDV1-fixed group effect by farmer suggests that small farmers are not a homogeneous group in term of their probabilities of LCC and that further classification of farmers into homogeneous subgroups will depict better their LCC decisions. Changes in the total area of landholdings proved a stronger influence in farmer's LCC decisions in their main property (primary lot) when compared to changes in the area of the primary lot. Panel data analysis of the LCC empirical transition probabilities (LSDV1 fixed time effects model) does not find enough evidence to prefer the fixed time effects model when compared to a Ordinary Least Square (OLS) pooled version of the probabilities. When applying the results of the panel data analysis to a modified markov chain model the LSDV1-farmer model provided a slightly better accuracy (59.25% accuracy) than the LSDV1-time and the OLS-pooled models (57.54% and 57.18%, respectively). The main finding for policy and planning purposes is that owners type 1—with stable total landholdings over time—tend to preserve forest with a much higher probability (0.9033) than owner with subdividing or expanding properties (probs. of 0.0013 and 0.0030). The main implication for policy making and planning is to encourage primary forest preservation, given that the Markov chain analysis shows that primary forest changes into another land cover, it will never go back to this original land cover class. Policy and planning recommendations are provided to encourage owner type 1 to continue their pattern of high forest conservation rates. Some recommendations include: securing land titling, providing health care and alternative sources of income for the OT1's family members and elderly owners to remain in the lot. Future research is encouraged to explore spatial autocorrelation in the pixel's probabilities of land cover change, effects of local policies and macro-economic variables in the farmer's LCC decisions. / Ph. D.

The aim of this thesis is to provide an overview of methods used in cause-of-death mortality analysis and to demonstrate the application on real data. In Chapter 1 we present the continuous model based on the force of mortality and review the approach using copula functions. In Chapter 2 we focus on the multinomial logit model formulated for cause-specific mortality data, discuss life tables construction and derive life expectancy. In Chapter 3 we apply the multinomial logit model on the data from Czech Statistical Office. We identify the regression model, check its assumptions, present the outputs including the fitted life expectancy, and predicted mortality rates. Later in Chapter 3 we consider several stress scenarios in order to demonstrate the impact of shocked mortality rates on the life expectancy.

一般而言， 銀行決定貸放與否，除了取決於客戶所提供之擔保品、保證 人之財力大小外， 徵信工作人員憑著自己過去經驗主觀之判定亦是一重 要之影響因素。 然而人為所牽涉之因素過於複雜且多變，若稍不謹慎極 可能犯下極大的錯失。 也因此銀行必須發展一套客觀之信用評估模型來 分析客戶的信用，以求降低放款的風險。而一般國內之銀行信用評估模型 及理論大多以混和行業別為研究對象，且多以財務比率分析為主。 但不 同的產業其財務特性有所差異，因此本研究便試圖以塑膠業建立單一產業 之信用評估模型， 以尋求建立更適合之信用評估模型。本研究首先根據 國內外文獻中整理出可供研究之若干財務變數， 再參酌目前銀行所使用 作為徵信評估之財務變數， 由於本模式是採本國塑膠業之資料， 且應用 對象亦主要針對本國銀行，故決定採用目前省屬行庫做徵信評估時所採行 的 17 個財務變數做分析。而後再以塑膠加工業為研究對象， 蒐集民國 79 年至 81 年三十家違約公司與五十七家履約公司共八十七家公司的財 務報表資料以計算其財務比率， 接著便計算此 17 個財務變數是否符合 常態分配之假設，以決定後續所採行之統計方法應為何。 另外根據因素 分析與不採用因素分析分別進行變數之選擇及相關性之分析，以尋找具有 解釋能力之財務變數。 最後以上述所得到之財務變數做自變數， 並將樣 本區分為原始樣本、預測樣本以及全體樣本兩群分別建立 Logit 迴歸模 型，以得到不同分析方法之總正確率、型一誤差、型二誤差及加權效率性 。 爾後再根據結果做較佳模式之選擇與評估，並加以解釋。根據實證結 果顯示， 依不同樣本及不同方法所建立的四個放款信用評估模型中， 經 比較其總正確率及加權效率性的高低後，採用因素分析的模型將優於不採 用因素分析的模型， 而全體樣本所建立的模型會優於原始樣本所建立的 模型。 而由此所得到最佳的信用評估模型為以全體樣本採用因素分析所 得的模型。另外， 本研究是以塑膠業單一產業為研究對象來建立信用評 估模型，與過去採混和產業別的分析有所不同。 而與過去的分析相較後 ，得知其正確區別效果確實較混和產業別所建立的模型為佳。

華人社會傳承了儒家文化思想的價值觀，但是在社會環境、政治體制及人口結構等因素的差異之下，衍生出各種文化特性的差異。本研究使用世界價值觀調查（World Values Survey，WVS）資料，比較不同社會中的華人在文化特性方面的差異，並分析文化特性及其他因素對主觀福祉（subjective well-being）的影響。 本研究採用世界價值觀調查所建立之社會價值觀（societal values），加入其他文化特徵、社會態度及個人生活觀衡量文化特性。本研究使用之計量模型為排序羅吉特迴歸（ordered logit regression）模型，分析上述變數對華人生活滿意度（life satisfaction）與快樂程度（happiness）的影響。本研究之目的在於找出影響華人主觀福祉之重要決定因素，並探討文化因素在收入與主觀福祉之間是否存在調節效果（moderating effect）。 實證結果顯示，文化會影響華人主觀福祉，並在收入與主觀福祉之間造成調節效果。而華人在各個社會中所衍生出的文化特性差異，也使得各個社會中華人主觀福祉的重要決定因素有所差異。 / Chinese societies inherited the values of Confucianism. However, these societies vary in their social environment, political system and demographic structure, thus result in differences in their cultural characteristics. This study uses the data from World Values Survey, to explore the differences among Chinese in different societies, and to analyze the determining factors of Chinese subjective well-being. In this study, the societal values established by the World Values Survey are used to represent the cultural properties. We also added other cultural traits, social attitudes and individual viewpoints for our analysis. In this study, we use ordered logit regression model to find out the important determinants of Chinese subjective well-being in different societies, and discuss whether there exists a cultural moderating effect between income and subjective well-being. Empirical results show that culture has a significant impact on subjective well-being of Chinese, and the effect of income on Chinese subjective well-being are also moderated by cultural properties. The differences between these Chinese societies also result in divergences of important determinants of Chinese subjective well-being between societies.

This bachelor's thesis in applied mathematics &amp; industrial economics is an attempt to model drives in American football using Markov chains. The transition matrix is obtained through logit regression analysis on historical data from the NFL. Different outcomes of drives are modelled as separate absorbing states in the Markov chain. Absorption probabilities are calculated representing the probabilities of each outcome. Results are tested against a Markov chain with the transition matrix based on frequency analysis. Three scoring rules unanimously declare the regression based model to be superior. The application of the model pertains to live sports betting. With the insight provided by the Markovian model, a bettor should be able to make statistically informed betting decisions. The prospect of creating a start-up based on the Markovian betting model is discussed. / Denna kandidatuppsats i tillämpad matematik &amp; industriell ekonomi är ett försök till att modellera drives i amerikansk fotboll med hjälp av Markovkedjor. Övergångsmatrisen fås genom logit-regressionsanalys av historisk data från NFL. Olika utfall av drives modelleras som separata absorberande tillstånd i Markovkedjan. Absorptionssannolikheter beräknas, vilka representerar sannolikheterna för de olika utfallen. Resultaten testas mot en Markovkedja där övergångsmatrisen fås genom frekvensanalys. Tre olika poängregler föredrar enhälligt den regressionsbaserade modellen. Modellens tillämpning berör sportbetting. Med hjälp av Markovmodellen bör en spelare kunna ta statistiskt underbyggda beslut i deras betting. Möjligheterna att skapa ett företag baserat på Markovmodellen diskuteras.