本研究為虧損減資相關之研究，以2000-2010年辦理虧損減資上市公司為樣本，並將樣本分組為只辦理虧損減資與減資再私募兩組，採用事件研究法比較兩者在減資宣告後的股價反應，並以前一年每季的財務指標分析兩者減資宣告前的營運績效。最後以羅吉斯迴歸模式，檢驗財務指標是否對公司減資政策選擇(減資後再私募與否)造成影響。 實證結果顯示，虧損減資公司在減資宣告有負向宣告效果，市場對虧損減資宣告為利空反應。樣本分組後發現，減資再私募公司在減資宣告後的累積負向報酬率較只辦理虧損減資公司為低。而減資再私募公司前一年每季的財務指標無論在資本結構、償債能力、獲利能力與經營能力均遜於單純只辦理虧損減資的公司。最後，羅吉斯迴歸模式顯示影響公司減資政策的因子為減資比率、減資宣告前一季季底P/B與每股盈餘。減資比率與前一季P/B越高，公司傾向採用減資再私募。前一季每股盈餘越高，公司傾向採用只辦理虧損減資，而迴歸模式預測的正確區別率約達到68.3% / It is an empirical study of capital-reduction. The sample of this study consists of companies which had utilized capital reduction in 2000 to 2010. The research employs the event-study method to examine the stock price reaction after the announcement of capital-reduction. The sample is divided into two subsamples, one includes companies with capital-reduction without private equity placement and the other are those companies with private equity placement after capital reduction. We compare not only their stock price reactions after the event but also the financial ratios before the event. Finally, we use Logit regression model to examine the impact of financial ratios to the company’s selection of capital-reduction policy. The results show that the stock price reaction after the event is negative; market is worried about the announcement of the capital-reduction. The cumulative returns of the companies utilized capital-reduction with private equity placement are lower than those of the companies without private equity placement after capital reduction. And the study of financial ratios shows that the operation performances of capital-reducing companies with private equity placement are worse than those of the capital-reducing companies with private equity placement. The result of Logit regression model shows that the influencing factors of company’s capital-reduction policy are previous quarter EPS, previous quarter P/B ratio, and the degree of capital-reduction. The higher the previous P/B and the degree of capital reduction, the higher possibility for capital-reducing company use the private equity placement afterwards. The higher the previous EPS, the lower possibility for the them to further to adopt the private equity placement.

Key sources of oil for western markets are located in complex geopolitical environments that increase economic and social risk. The amalgamation of economic, environmental, social and national security concerns for petroleum-based economies have created a renewed emphasis on alternative sources of energy which include biomass. The stability of sustainable biomass markets hinges on improved methods to predict and visualize business risk and cost to the supply chain. This thesis develops Bayesian logistic regression models, with comparisons of classical maximum likelihood models, to quantify significant factors that influence the siting of biomass-using facilities and predict potential locations in the 13-state Southeastern United States for three types of biomass-using facilities. Group I combines all biomass-using mills, biorefineries using agricultural residues and wood-using bioenergy/biofuels plants. Group II included pulp and paper mills, and biorefineries that use agricultural and wood residues. Group III included food processing mills and biorefineries that use agricultural and wood residues. The resolution of this research is the 5-digit ZIP Code Tabulation Area (ZCTA), and there are 9,416 ZCTAs in the 13-state Southeastern study region. For both classical and Bayesian approaches, a training set of data was used plus a separate validation (hold out) set of data using a pseudo-random number-generating function in SAS® Enterprise Miner. Four predefined priors are constructed. Bayesian estimation assuming a Gaussian prior distribution provides the highest correct classification rate of 86.40% for Group I; Bayesian methods assuming the non-informative uniform prior has the highest correct classification rate of 95.97% for Group II; and Bayesian methods assuming a Gaussian prior gives the highest correct classification rate of 92.67% for Group III. Given the comparative low sensitivity for Group II and Group III, a hybrid model that integrates classification trees and local Bayesian logistic regression was developed as part of this research to further improve the predictive power. The hybrid model increases the sensitivity of Group II from 58.54% to 64.40%, and improves both of the specificity and sensitivity significantly for Group III from 98.69% to 99.42% and 39.35% to 46.45%, respectively. Twenty-five optimal locations for the biomass-using facility groupings at the 5-digit ZCTA resolution, based upon the best fitted Bayesian logistic regression model and the hybrid model, are predicted and plotted for the 13-state Southeastern study region.

<p>This study employs the Human Capital Theory (HCT), which postulates that the education of women is positively related to the likelihood of their labour force participation, in order to investigate quarterly dynamics in the labour force. This approach is an advancement of knowledge gained from previous studies such as Serumanga-Zake and Kotze (2004) and Ntuli (2004) who investigated the annual dynamics in FLFP. Investigating quarterly dynamics in FLFP is prudent as the market economy is very dynamic particularly at a point when the world economy is experiencing recession. Data for the study are extracted from the 2008 Quarterly Labour Force Survey conducted by Statistics South Africa. Logistic regression analysis modeling was employed with the dependent variable, FLFP, as a binary outcome. Other variables controlled in the analysis are gender, population group, age, marital status, education status, sector, main industry, main occupation and province. The results show that there is association between education status and FLFP status. Findings from this research are expected to contribute to the knowledge about trends in FLFP in South Africa and aid in planning of interventions aimed at improving the status of women as one of the critical steps in achieving the Millennium Development Goals.</p>

<p>This study assesses the present level of household water access and the willingness to pay in South Africa. Although the general literature informs that progress has been made in positing South Africa above the levels found in most African countries, there are some marked inequalities among the population groups and across the provinces, with some performing well and others poorly in this regard. The study looks at the extent to which households differ in terms of water access and willingness to pay according to the province of residence. The study focuses on household heads / male and female, through different social and demographic attributes, by taking account of variables such as age, education&nbsp / attainment, geographic areas, and population group to name but a few. The data used in this study comes from the 2007 General Household Survey (GHS) conducted by Statistics South Africa. The scope is national and employs cross tabulation and logistic regression to establish relationships and the likelihood of living in a household with access to safe&nbsp / drinking water in South Africa. Results presented in this study suggest that the difference is determined by socio- demographic characteristics of each household such as age, gender, population group, level of education, employment status income, dwelling unit, dwelling ownership, living quarters,household size and income. It throws more light as to what needs to be taken into account when considering demand and supply of and priorities for water intervention from the household perspective.</p>

The recent availability of geo-enabled web-based tools creates new possibilities for facilitating carpool formation. Carpool Zone is a web-based carpool formation service offered by Metrolinx, the transportation planning authority for the Greater Toronto and Hamilton Area (GTHA), Canada. The carpooling literature has yet to uncover how different built environments may facilitate or act as barriers to carpool propensity. This research explores the relationship between the built environment and carpool formation. With respect to the built environment, industrial and business parks (homogeneous land-use mix) are associated with high odds of forming carpools. The results suggest that employer transport policies are also among the more salient factors influencing carpool formation and use. Importantly, the findings indicate that firms interested in promoting carpooling will require contingencies to reduce the uncertainty of ride provision that may hamper long-term carpool adoption by employees.

The recent availability of geo-enabled web-based tools creates new possibilities for facilitating carpool formation. Carpool Zone is a web-based carpool formation service offered by Metrolinx, the transportation planning authority for the Greater Toronto and Hamilton Area (GTHA), Canada. The carpooling literature has yet to uncover how different built environments may facilitate or act as barriers to carpool propensity. This research explores the relationship between the built environment and carpool formation. With respect to the built environment, industrial and business parks (homogeneous land-use mix) are associated with high odds of forming carpools. The results suggest that employer transport policies are also among the more salient factors influencing carpool formation and use. Importantly, the findings indicate that firms interested in promoting carpooling will require contingencies to reduce the uncertainty of ride provision that may hamper long-term carpool adoption by employees.

Målet med den här studien är att undersöka hur barn under 15 år påverkades av Cryptosporidiumutbrottet i slutet av år 2010 i Östersunds kommun. Datamaterialet utgörs av svar på en enkätundersökning från 514 barn rörande deras hälsa relaterad till utbrottet. Dessa enkäter togs fram av svenska Smittskyddsinstitutet kort efter utbrottet och det är i uppdrag av denna myndighet som studien utförs. Analys av riskfaktorer och följdsymptom utförs med logistiska regressionsmodeller utifrån både ett Bayesianskt och ett frekventistiskt tillvägagångssätt för att på så sätt betrakta datamaterialet från fler synvinklar och samtidigt identifiera skillnader mellan dessa två tillvägagångssätten. En annan del av arbetet presenterar bortfallskalibrerade skattningar av antalet Cryptosporidiumfall både totalt och månadsvis men också skattningar av fallprevalensen i olika redovisningsgrupper. Slutligen analyseras sambanden mellan följdsymptomen med logistisk regression. Dessutom utförs variabelklustring av följdsymptom med metoden fuzzy clustering för att se hur dessa kan grupperas. Resultaten visar att Glas vatten, Inom VA. område, Tidigare lös avföring och Kön identifieras som riskfaktorer medan de bäst förklarande följdsymptomen är Vattnig diarré, Buk- eller magsmärtor, Feber och Trött/utmattad. / The purpose of this study is to analyze how children under the age of 15 years were affected by the 2010 Östersund Cryptosporidium outbreak. The data consists of responses to a questionnaire from 514 children concerning their health related to the outbreak. The questionnaire was developed by the Swedish Institute for Infectious Disease Control shortly after the outbreak. The analysis of risk factors and the analysis of symptoms associated with infection were performed using logistic regression models based on both a Bayesian and a frequentist approach. Using the two different approaches we thus consider the dataset from different angels and at the same time try to identify the differences between these two approaches. Another part of the paper presents estimates calibrated for nonresponse of the number of Cryptosporidium infections both totally and on a monthly basis. Additionally estimates of the prevalence of cases in various domain groups are presented. Finally, associations between the symptoms are investigated using logistic regression. With the same goal we performed variable clustering of the symptoms using the fuzzy clustering approach. The results shows that higher water intake, getting water thru the municipal water distribution system, Former loose stools and Gender could be identified as risk factors while the best-explanatory symptoms were watery diarrhea, abdominal or stomach pain, fever and tiredness/exhaustion.

In this study, we consider high frequency transaction data of NYSE, and apply statistical methods to characterize each trade into two classes, influential and ordinary liquidity trades. First, a median based approach is used to establish a high R-square price-volume model for high frequency data. Next, transactions are classified into four states based on the trade price, trade volume, quotes, and quoted depth. Volume weighted transition probability of the four states are investigated and shown to be distinct for informed trades and ordinary liquidity trades. Furthermore, four market reaction factors are introduced and studied. Logistic regression models of the influential trades are established based on the four factors and odds ratios are used to select the cutoff points.

Financial failure is an important subject for both the economical development of the country and for the self - evaluation of individual companies. Increase in the number of financially failed companies points out the misuse of the country resources. Recently, financial failure threatens both small and large companies in Turkey. It is important to determine factors that affect the financial failure by analyzing models and to use these models for auditing the financial situation. In today&rsquo / s Turkey, the statistical methods that are used for this purpose involve single level models applied to cross-sectional data. However, multilevel models applied to panel data are more preferable as they gather more information, and also, enable the calculated financial success probabilities to be more trustworthy. In this thesis, publicly available panel data that are collected from The Istanbul Stock Exchange are investigated. Mainly, financial success of companies from two sectors, namely industry and services, are investigated. For the analysis of this panel data, data exploration methods, missing data imputation, possible solutions to multicollinearity problem, single level logistic regression models and multilevel models are used. By these models, financial success probabilities for each company are calculated / the factors related to the financial failure are determined, and changes in time are observed. Models and early warning systems resulted in correct classification rates of up to 100%. In the services sector, a small number of companies having publicly available data result in a decline in the success of models. It is concluded that sharing data with more subjects observed in a longer time period collected in the same format with academicians, will result in better justified outputs, which are useful for both academicians and managers.

Epidemiological data with disease characteristic information can be modelled in several ways. One way is taking each disease characteristic as a response and constructing binary or polytomous logistic regression model. Second way is using a new response which consists of disease subtypes created by cross-classification of disease characteristic levels, and then constructing polytomous logistic regression model. The former may be disadvantageous since any possible covariation between disease characteristics is neglected, whereas the latter can capture that covariation behaviour. However, cross-classifying the characteristic levels increases the number of categories of response, so that dimensionality problem in parameter space may occur in classical polytomous logistic regression model. A two staged polytomous logistic regression model overcomes that dimensionality problem. In this thesis, study is progressen in two main directions: simulation study and data analysis parts. In simulation study, models that capture the covariation behaviour are compared in terms of the response model parameter estimators. That is, performances of the maximum likelihood estimation (MLE) approach to classical polytomous logistic regression, Bayesian estimation approach to classical polytomous logistic regression and pseudo-conditional likelihood (PCL) estimation approach to two stage polytomous logistic regression are compared in terms of bias and variation of estimators. Results of the simulation study revealed that for small sized sample and small number of disease subtypes, PCL outperforms in terms of bias and variance. For medium scaled size of total disease subtypes situation when sample size is small, PCL performs better than MLE, however when the sample size gets larger MLE has better performance in terms of standard errors of estimates. In addition, sampling variance of PCL estimators of two stage model converges to asymptotic variance faster than the ML estimators of classical polytomous logistic regression model. In data analysis, etiologic heterogeneity in breast cancer subtypes of Turkish female cancer patients is investigated, and the superiority of the two stage polytomous logistic regression model over the classical polytomous logistic model with disease subtypes is represented in terms of the interpretation of parameters and convenience in hypothesis testing.