The design of clinical trials is one of the important problems in medical statistics. Its main purpose is to determine the methodology and the sample size required of a testing study to examine the safety and efficacy of drugs. It is also a part of the Food and Drug Administration approval process. In this thesis, we first study the comparison of the efficacy of drugs in clinical trials. We focus on the two-sample comparison of proportions to investigate testing strategies based on two-stage design. The properties and advantages of the procedures from the proposed testing designs are demonstrated by numerical results, where comparison with the classical method is made under the same sample size. A real example discussed in Cardenal et al. (1999) is provided to explain how the methods may be used in practice. Some figures are also presented to illustrate the pattern changes of the power functions of these methods. In addition, the proposed procedure is also compared with the Pocock (1997) and O¡¦Brien and Fleming (1979) tests based on the standardized statistics. In the second part of this work, the operating room scheduling problem is considered, which is also important in medical studies. The national health insurance system has been conducted more than ten years in Taiwan. The Bureau of National Health Insurance continues to improve the national health insurance system and try to establish a reasonable fee ratio for people in different income ranges. In accordance to the adjustment of the national health insurance system, hospitals must pay more attention to control the running cost. One of the major hospital's revenues is generated by its surgery center operations. In order to maintain financial balance, effective operating room management is necessary. For this topic, this study focuses on the model fitting of operating times and operating room scheduling. Log-normal and mixture log-normal distributions are identified to be acceptable statistically in describing these operating times. The procedure is illustrated through analysis of thirteen operations performed in the gynecology department of a major teaching hospital in southern Taiwan. The best fitting distributions are used to evaluate performances of some operating combinations on daily schedule, which occurred in real data. The fitted distributions are selected through certain information criteria and bootstrapping the log-likelihood ratio test. Moreover, we also classify the operations into three different categories as well as three stages for each operation. Then based on the classification, a strategy of efficient scheduling is proposed. The benefits of rescheduling based on the proposed strategy are compared with the original scheduling observed.

There are many reasons for companies to hold real estate, including for operating business, production, sales, and providing services. Previous researches show that corporate real estate (CRE) is an important part of company assets, and it will affect stock returns and risk of company. The main object of this study is to investigate the impact of changes in CRE on stock returns and risk of company in Taiwan. Moreover, this study analyzes how CRE affect toward different industry during each business cycle period. Then, we provide some suggestions to stockholders and managers. The data set from 1992 through 2011 in Taiwan stock market, the relationship between CRE and stock returns and risk are analyzed using two stage least squares regression model. The empirical results show that, on average, higher CRE appears to be associated with higher abnormal return performance and higher total risk. On the other hand, CRE show negative impact on business operation such as lower adjusted return on assets and higher risk of bankruptcy. Furthermore, CRE factor is associated with higher abnormal return performance and higher firm value when company with small asset size, high P/E ratio or newly establish characters. Results also indicate that the impact of CRE on firm¡¦s stock price and risk depend on industries, business cycle period, and firm characters. CRE show negative impact on Textile, Tourism, and Trading and Consumers' Goods Industry. In Food Industry, higher CRE factor is associated with lower system risk and positive impact on business operation.

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.

The two-stage data mining technique for classifications in tourism recommendation system is necessary to connect user perception, decision criteria and decision purpose. In existed literature, hybrid data mining method combining Decision Tree and K-nearest neighbour approaches (DTKNN) were proposed. It has a high precision rate of approximately 80% in K-nearest Neighbour (KNN) but a much lower rate in the first stage using Decision Tree (Fu & Tu, 2011). It included two potential improvements on two-stage technique. To improve the first stage of DTKNN in precision rate and the efficiency, the amount of questions is decreased when users search for the desired recommendation on the system. In this paper, the researcher investigates the way to improve the first stage of DTKNN for full questionnaires and also determines the suitability of dynamic questionnaire based on its precision rate in future tourism recommendation system. Firstly, this study compared and chose the highest precision rate among Decision Tree, Multi-staged Binary Tree and Back Propagation of Error Neural Network (BPNN). The chosen method is then combined with KNN to propose a new methodology. Secondly, the study compared and deter¬mined the suitability of dynamic questionnaires for all three classification methods by decreasing the number of attributes. The suitable dynamic questionnaire is based on the least amount of attributes used with an appropriate precision rate. Tourism recommendation system is selected as the target to apply and analyse the usefulness of the algorithm as tourism selection is a two-stage example. Tourism selection is to determine expected goal and experience before going on a tour at the first stage and to choose the tour that best matches stage one. The result indicates that Multi-staged Bi¬nary Tree has the highest precision rate of 74.167% comparing to Decision Tree with 73.33% then BPNN with 65.47% for full questionnaire. This new approach will improve the effectiveness of the system by improving the precision rate of first stage under the current DTKNN method. For dynamic questionnaire, the result has shown that Decision Tree is the most suitable method given that it resulted in the least difference of 1.33% in precision rate comparing to full questionnaire, as opposed to 1.48% for BPNN and 4% for Multi-staged Binary Tree. Thus, dynamic questionnaire will also improve the efficiency by decreasing the amount of questions which users are required to fill in when searching for the desired recommendation on the system. It provides users with the option to not answer some questions. It also increases the practicality of non-dynamic questionnaire and, therefore, affects the ultimate precision rate.

In view of the current situation of fluctuating high crude oil prices, it is now more important than ever for petroleum refineries to operate at an optimal level in the present dynamic global economy. Acknowledging the shortcomings of deterministic models, this work proposes a hybrid of stochastic programming formulations for an optimal midterm refinery planning that addresses three factors of uncertainties, namely price of crude oil and saleable products, product demand, and production yields. An explicit stochastic programming technique is utilized by employing compensating slack variables to account for violations of constraints in order to increase model tractability. Four approaches are considered to ensure both solution and model robustness: (1) the Markowitz???s mean???variance (MV) model to handle randomness in the objective coefficients of prices by minimizing variance of the expected value of the random coefficients; (2) the two-stage stochastic programming with fixed recourse approach via scenario analysis to model randomness in the right-hand side and left-hand side coefficients by minimizing the expected recourse penalty costs due to constraints??? violations; (3) incorporation of the MV model within the framework developed in Approach 2 to minimize both the expectation and variance of the recourse costs; and (4) reformulation of the model in Approach 3 by adopting mean-absolute deviation (MAD) as the risk metric imposed by the recourse costs for a novel application to the petroleum refining industry. A representative numerical example is illustrated with the resulting outcome of higher net profits and increased robustness in solutions proposed by the stochastic models.

In the push for clean and renewable fuels, timber derived biomass is a promising frontier for biofuel production in the United States. This thesis approaches the established timberlands biofuel implementation problem with three different mathematical programming studies, each testing feasibility and sustainability in different economic and supply related situations. In the first study, a competitive game theory approach was utilized to provide new insights into the behavior within a timberlands supply chain. We utilized Stackelberg game theory modeled with bilevel programming to represent the competing harvesting and manufacturing sectors. In the second study, the initial bilevel model was utilized in a larger two stage multiperiod model with parameter uncertainty. In this more realistic model, the first stage contained logistical decisions around biorefinery investments, such as location and capacity, while the second stage was composed of multiple discrete bilevel scenarios representing potential situations in the timberlands system. The final study focused on long term land management strategies for the timberlands supply chain. Introduction of a new biorefinery investment meant that management strategies must be altered to ensure consistent material flows to manufacturers as well as sustain the new production facility. A modified cyclic scheduling formulation was used to model a timberlands system and its planting and harvesting schedule to accommodate a new biorefinery. This cyclic model added an initial startup period to initiate biofuel production and provide time to adapt land management. The overall contribution of these studies was to analyze a biorefinery's impact on the established behavior in a timberlands supply chain. In particular, the goals of these models were to develop introductory decision making tools for timberlands supply chain managers.

傳統DEA效率分析假設產業具單一生產過程，直到2000年，Färe and Grosskopf 提出Network DEA，闡明產業生產過程應分屬多階段性質。本研究應用其架構，假設職棒產業生產過程為兩個階段，並特別以Sexton and Lewis (2003) 增加中間產出的Two-Stage DEA 法，即第一階段的產出作為第二階段的投入，進行1992年至2004年「中華職業棒球聯盟」，共71個DMUs的實證分析。 Two-Stage DEA的第一階段是指花錢聘雇球員，而球團有效花錢聘雇球員的程度稱為「前置效率」，效率平均值為0.994；第二階段效率是指球隊正式比賽時，球員是否充分發揮技術潛能贏球，稱為「臨場效率」，效率平均值為0.969；而包含第一、二階段的整體球團運作效率則為「組織效率」，效率平均值為0.798。透過與傳統DEA的BCC模型之比較，發現Two-Stage DEA提供較豐富的組織運作過程資訊，俾管理者找出球團之無效率階段。 復以Tobit截斷迴歸模型，探討影響球團「前置效率」、「臨場效率」與「組織效率」之變數為何。結果指出，球隊對戰觀眾數、聯盟變革與現場直播對「前置效率」有顯著影響，「臨場效率」則受到臨時性獎勵制度與投手平均年齡的影響，而對戰觀眾數、聯盟變革、總教練的更動頻率與常設性獎勵制度，則是造成球隊「組織效率」差異的主因。 關鍵字：Network DEA、中間產出、Two-Stage DEA、職業棒球、效率 / Traditional DEA gauges efficiencies with only one production process, while in this study we apply Network DEA initiated by Färe and Grosskopf (2000), and in particular follow the Two-Stage DEA model incorporating the intermediate products, outputs from the first stage becomes inputs to the second stage, by Sexton and Lewis (2003) to evaluate the production efficiency of 71 DMUs of the Chinese professional Baseball League (CPBL ) from 1992 to 2004. How fair are the ball teams paying the players is called ”front office efficiency”, arithmetic mean is 0.994, in the first stage, how potentially successful are the teams playing the games is called “on-field efficiency”, arithmetic mean is 0.969, in the second stage, and how potentially successful are the teams playing the games if with perfect front office efficiency of the teams is called “organization efficiency”, arithmetic mean is 0.798. Comparing Two-Stage DEA model with BCC model of traditional DEA, we find that the former model provides more information of organizational operations for managers to understand and better the performance of the teams. Tobit regression analysis shows that (1) the front office efficiency is significantly positively influenced by spectators, variation of the league and television live, and (2) the on-field efficiency is significantly positively influenced by extemporaneous bonus and pitcher’s age. (3) The organization efficiency is significantly positively influenced by the spectators, variation of the league and fixed bonus, but the organization efficiency is significantly negatively influenced by change of coaches. Keyword：Network DEA, Intermediate products, Two-Stage DEA , Efficiency, Professional baseball

Submitted by Jos? Henrique Henrique (jose.neves@ufvjm.edu.br) on 2017-06-06T21:08:54Z No. of bitstreams: 2 jose_lucas_camargos.pdf: 1772896 bytes, checksum: ed61126d3964190579d1888b21edf3aa (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Rodrigo Martins Cruz (rodrigo.cruz@ufvjm.edu.br) on 2017-06-22T15:30:03Z (GMT) No. of bitstreams: 2 jose_lucas_camargos.pdf: 1772896 bytes, checksum: ed61126d3964190579d1888b21edf3aa (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-06-22T15:30:03Z (GMT). No. of bitstreams: 2 jose_lucas_camargos.pdf: 1772896 bytes, checksum: ed61126d3964190579d1888b21edf3aa (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES) / Funda??o de Amparo ? Pesquisa do Estado de Minas Gerais (FAPEMIG) / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico (CNPq) / O objetivo deste trabalho foi avaliar a influ?ncia da redu??o de parcelas em algumas medi??es de invent?rio florestal cont?nuo na modelagem do crescimento e produ??o de um povoamento de Eucalyptus sp. O modelo de Clutter foi utilizado na modelagem do crescimento e da produ??o florestal, a partir de dados selecionados de 374 parcelas distribu?das em 172 talh?es. Foram utilizadas tr?s alternativas de modelagem: sem estratifica??o do povoamento, com estratifica??o do povoamento e um conjunto de 30 simula??es. Estas simula??es foram amostras de 40 parcelas aleat?rias cada, sem estratifica??o, com o objetivo de possibilitar uma an?lise geral dos efeitos do fen?meno estudado. Em todas as alternativas, foi realizada a modelagem do crescimento e da produ??o sem considerar redu??es nas medi??es de invent?rio florestal cont?nuo e considerando tr?s situa??es de redu??o. As situa??es consideradas foram medi??es de 25%, 50% e 75% das parcelas do invent?rio florestal cont?nuo nas medi??es dois e quatro, e 100% nas medi??es um, tr?s e cinco. Esta mesma metodologia foi repetida, por?m com a utiliza??o do modelo de regress?o n?o linear Log?stico, acrescido de um fator de corre??o. Ambos os modelos apresentaram boa precis?o para a predi??o e proje??o do volume do povoamento em idades futuras. O modelo Log?stico com fator de corre??o, entretanto, foi mais preciso que o modelo de Clutter. Para ambos os modelos foi constatado que n?o houveram perdas significativas de precis?o acarretadas pelas redu??es no n?mero de parcelas em algumas medi??es do invent?rio florestal cont?nuo. Para a modelagem do crescimento e da produ??o, ? recomendado que estas redu??es sejam criteriosas e que preferencialmente a ?ltima medi??o n?o seja reduzida. / Disserta??o (Mestrado) ? Programa de P?s-Gradua??o em Ci?ncia Florestal, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2017. / This study had as object to evaluate the influence of the reduction of plots at some measurements of continuous forest inventory in the growth and yield modeling of a Eucalyptus sp. forest stand. The Clutter model was used in forest growth and yield modeling, using selected data of 374 distributed plots in 172 forest compartments. It were used three modeling alternatives: without stratification of the forest stand, with stratification of the forest stand and a set of 30 simulations. These simulations were samples of 40 plots each one, without stratification, objectifying a general analysis about the studied phenomenon. To all the alternatives, it was made the forest growth and yield modeling without considering reductions in continuous forest inventory measurements and considering three reduction situations. The considered situations were measurements of 25%, 50% and 75% of the continuous forest inventory plots in the measures two and four, and 100% in the measures one, three and five. This same methodology was repeated, but using the Logistic nonlinear regression model, plus a correction factor. Both models had good precision to predicting and projecting the forest stand volume at future ages. The Logistic model with a correcting factor, however, had best precision than the Clutter model. For both models it was found that there was not significant losses of accuracy caused by reductions in the number of plots at some continuous forest inventory measurements. To forest growth and yield modeling, it is recommended for these reductions to be judicious and preferably the last measurement to not be reduced.

Orientador: Prof. Dr. Marat Rafikov / Tese ( doutorado)- Universidade Federal do ABC. Programa de Pós-Graduação em Energia, 2016.

The prospects for the next Generation - whether young people, regardless of their backgrounds, have equal chances of social success - pose a momentous problem for modern societies. Inequality of opportunity, often reflected by social immobility, is a threat to the egalitarian promise and the stability of your society. This work argues that social capital transmission plays an important role for the chances of social success in Western societies. For the example of Germany, it is reasoned that weak social capital environments deepen existing disadvantages. Even though all levels of education are easily accessible and affordable, Germany has one of the lowest levels of educational mobility among the industrialized countries of the world. Problems appear to be systematic, since the decision regarding entry into higher secondary education is made at early age and is left, in most cases, with the parents, who rely on their own educational trajectory. Outside of the school environment, differences in social capital inheritance explain educational immobility. With the use of the German Socio-Economic Panel survey from 1984 to 2014, various analyses about the relation between social capital and educational success are performed. Social capital, which is helpful for educational and social success, clearly depends on the educational family background. This indirect link has been disregarded in past contributions. Alternative forms of schooling, such as comprehensive and all-day education, as well as a delay of the decision regarding entry into higher education, could help improving unequal social capital inheritance.