This thesis goal is to analyze the factors that are influencing the strategies and growth expectations of the Czech start-ups. The analysis is based on the data sample from the Czech GEM 2011 and unique study of the Czech start-ups performed in 2011 as well. Various statistical methods are used to determine which microeconomic factors affect (1) the strategy choice and (2) growth expectations. As for the factors influencing expected growth, the research is concluded by the creation of the regression equations explaining the expected growth in the revenues as well as expected growth in employees. The factors influencing strategy choice are: managers-owners experience, the environment, self-efficacy, gender, and innovativeness. The factors influencing expected growth in revenues are: innovativeness, self-efficacy, competitive strategy, managers-owners experience, social capital, export intention, and business planning. The factors influencing the expected growth in employment are: legal structure of company, export intention, self-efficacy, innovativeness, competitive strategy, business planning. Following variables create the regression equation explaining expected growth in revenues: financial planning, export intention, education, IPR use, and age of entrepreneur. The variables explaining expected growth in employees are: (1) export intention, product newness, and legal structure of the company, (2) product newness, differentiation inclination, and financial projections. Alongside, the Porter's generic strategies framework was proved to fit the data sample while it was showed that a portion of Czech entrepreneurs are "stuck in the middle". By these findings, this thesis provides unique empirical verification of the growth and strategy theory on the sample of the Czech entrepreneurs.

Neste trabalho de tese propomos um modelo de regressão beta com erros de medida. Esta proposta é uma área inexplorada em modelos não lineares na presença de erros de medição. Abordamos metodologias de estimação, como máxima verossimilhança aproximada, máxima pseudo-verossimilhança aproximada e calibração da regressão. O método de máxima verossimilhança aproximada determina as estimativas maximizando diretamente o logaritmo da função de verossimilhança. O método de máxima pseudo-verossimilhança aproximada é utilizado quando a inferência em um determinado modelo envolve apenas alguns mas não todos os parâmetros. Nesse sentido, dizemos que o modelo apresenta parâmetros de interesse como também de perturbação. Quando substituímos a verdadeira covariável (variável não observada) por uma estimativa da esperança condicional da variável não observada dada a observada, o método é conhecido como calibração da regressão. Comparamos as metodologias de estimação mediante um estudo de simulação de Monte Carlo. Este estudo de simulação evidenciou que os métodos de máxima verossimilhança aproximada e máxima pseudo-verossimilhança aproximada tiveram melhor desempenho frente aos métodos de calibração da regressão e naïve (ingênuo). Utilizamos a linguagem de programação Ox (Doornik, 2011) como suporte computacional. Encontramos a distribuição assintótica dos estimadores, com o objetivo de calcular intervalos de confiança e testar hipóteses, tal como propõem Carroll et. al.(2006, Seção A.6.6), Guolo (2011) e Gong e Samaniego (1981). Ademais, são utilizadas as estatísticas da razão de verossimilhanças e gradiente para testar hipóteses. Num estudo de simulação realizado, avaliamos o desempenho dos testes da razão de verossimilhanças e gradiente. Desenvolvemos técnicas de diagnóstico para o modelo de regressão beta com erros de medida. Propomos o resíduo ponderado padronizado tal como definem Espinheira (2008) com o objetivo de verificar as suposições assumidas ao modelo e detectar pontos aberrantes. Medidas de influência global, tais como a distância de Cook generalizada e o afastamento da verossimilhança, são utilizadas para detectar pontos influentes. Além disso, utilizamos a técnica de influência local conformal sob três esquemas de perturbação (ponderação de casos, perturbação da variável resposta e perturbação da covariável com e sem erros de medida). Aplicamos nossos resultados a dois conjuntos de dados reais para exemplificar a teoria desenvolvida. Finalmente, apresentamos algumas conclusões e possíveis trabalhos futuros. / In this thesis, we propose a beta regression model with measurement error. Among nonlinear models with measurement error, such a model has not been studied extensively. Here, we discuss estimation methods such as maximum likelihood, pseudo-maximum likelihood, and regression calibration methods. The maximum likelihood method estimates parameters by directly maximizing the logarithm of the likelihood function. The pseudo-maximum likelihood method is used when the inference in a given model involves only some but not all parameters. Hence, we say that the model under study presents parameters of interest, as well as nuisance parameters. When we replace the true covariate (observed variable) with conditional estimates of the unobserved variable given the observed variable, the method is known as regression calibration. We compare the aforementioned estimation methods through a Monte Carlo simulation study. This simulation study shows that maximum likelihood and pseudo-maximum likelihood methods perform better than the calibration regression method and the naïve approach. We use the programming language Ox (Doornik, 2011) as a computational tool. We calculate the asymptotic distribution of estimators in order to calculate confidence intervals and test hypotheses, as proposed by Carroll et. al (2006, Section A.6.6), Guolo (2011) and Gong and Samaniego (1981). Moreover, we use the likelihood ratio and gradient statistics to test hypotheses. We carry out a simulation study to evaluate the performance of the likelihood ratio and gradient tests. We develop diagnostic tests for the beta regression model with measurement error. We propose weighted standardized residuals as defined by Espinheira (2008) to verify the assumptions made for the model and to detect outliers. The measures of global influence, such as the generalized Cook\'s distance and likelihood distance, are used to detect influential points. In addition, we use the conformal approach for evaluating local influence for three perturbation schemes: case-weight perturbation, respose variable perturbation, and perturbation in the covariate with and without measurement error. We apply our results to two sets of real data to illustrate the theory developed. Finally, we present our conclusions and possible future work.

Neste trabalho estudamos o modelo de regressão logística com erro de medida nas covariáveis. Abordamos as metodologias de estimação de máxima pseudoverossimilhança pelo algoritmo EM-Monte Carlo, calibração da regressão, SIMEX e naïve (ingênuo), método este que ignora o erro de medida. Comparamos os métodos em relação à estimação, através do viés e da raiz do erro quadrático médio, e em relação à predição de novas observações, através das medidas de desempenho sensibilidade, especificidade, verdadeiro preditivo positivo, verdadeiro preditivo negativo, acurácia e estatística de Kolmogorov-Smirnov. Os estudos de simulação evidenciam o melhor desempenho do método de máxima pseudoverossimilhança na estimação. Para as medidas de desempenho na predição não há diferença entre os métodos de estimação. Por fim, utilizamos nossos resultados em dois conjuntos de dados reais de diferentes áreas: área médica, cujo objetivo está na estimação da razão de chances, e área financeira, cujo intuito é a predição de novas observações. / We study the logistic model when explanatory variables are measured with error. Three estimation methods are presented, namely maximum pseudo-likelihood obtained through a Monte Carlo expectation-maximization type algorithm, regression calibration, SIMEX and naïve, which ignores the measurement error. These methods are compared through simulation. From the estimation point of view, we compare the different methods by evaluating their biases and root mean square errors. The predictive quality of the methods is evaluated based on sensitivity, specificity, positive and negative predictive values, accuracy and the Kolmogorov-Smirnov statistic. The simulation studies show that the best performing method is the maximum pseudo-likelihood method when the objective is to estimate the parameters. There is no difference among the estimation methods for predictive purposes. The results are illustrated in two real data sets from different application areas: medical area, whose goal is the estimation of the odds ratio, and financial area, whose goal is the prediction of new observations.

摘要 本文主要考慮群集樣本(clustered samples)的存活分析，而每一群集中又分為兩種組別(groups)。假定同群集同組別內的個體共享相同但不可觀測的隨機脆弱性(frailty)，因此面臨的是雙變量脆弱性變數的多變量存活資料。首先，驗證雙變量脆弱性對雙變量對數存活時間及雙變量存活時間之相關係數所造成的影響。接著，假定雙變量脆弱性服從雙變量對數常態分配，條件存活時間模式為韋伯迴歸模式，我們利用EM法則，推導出雙變量脆弱性之多變量存活模式中母數的估計方法。 關鍵詞：雙變量脆弱性，Weibull迴歸模式，對數常態分配，EM法則 / Abstract Consider survival analysis for clustered samples, where each cluster contains two groups. Assume that individuals within the same cluster and the same group share a common but unobservable random frailty. Hence, the focus of this work is on bivariate frailty model in analysis of multivariate survival data. First, we derive expressions for the correlation between the two survival times to show how the bivariate frailty affects these correlation coefficients. Then, the bivariate log-normal distribution is used to model the bivariate frailty. We modified EM algorithm to estimate the parameters for the Weibull regression model with bivariate log-normal frailty. Key words：bivariate frailty, Weibull regression model, log-normal distribution, EM algorithm.

本研究的目的為研究台灣省在1990年之15-17歲的在學率，在找出可能影響因素並蒐集好相關的資料後，我們將蒐集到的資料分成兩個部份，一個部份用來建造模型，而另一個部份則用來測試所建立出來的模型。主要的過程是：先利用簡單迴歸模型了解各個可能的因素對於15-17歲的在學率的影響程度，經過許多分析及了解後再對這些變數採取可能的變數轉換(variable transformations)，而後再利用三種常用的統計迴歸方法﹝包含有逐步迴歸(stepwise regression)方法、前進選擇(forward selection)方法以及後退消除(backward elimination)方法﹞去發展出一個適當的複迴歸模型(multiple regression model)。對於這個模型，以實際的台灣在學情況來看，我們看不出它有任何的不合理；同時也利用圖形及檢定去驗證模型的假設，其次還做有關迴歸參數的推論(inferences about regression parameters)。再其次，我們運用變異數分析的結果(analysis of variance results)以及新觀察值的預測情形(predictions of new observations)來評估模型的預測能力。最後並利用所得到的最適當的模型，對如何提昇15-17歲青少年的在學率給予適當的建議。 / The objective of this research is to study what factors may affect the schooling rates of 15-17 years old in Taiwan province in 1990. After finding out some possible factors and collecting those data regarding those factors, we separate the data (by stratified random sampling) into two sets. One set is used to construct the model, and the other set shall be used to test the model. The main process to build a regression model is as follows. First, we shall use simple linear regression models to help us to see if each factor may have relation with the schooling rates. With the analysis of residuals and so on, we then make appropriate transformations on each of these factors. Finally, we use three common statistical regression techniques (including stepwise regression, forward selection, and backward elimination methods) to develop a suitable multiple regression model. It seems that, by our understanding of schooling rates in Taiwan, this model is not unreasonable. In addition, we verify the assumptions of the model by graphical methods and statistical tests. We also do the inferences about regression parameters. Furthermore, ye use the results of the analysis of variance and predictions of new observations to evaluate the prediction ability of the model. Finally, we use the most appropriate multiple regression model to give some suggestions to improve (or keep) the schooling rates of 15-17 years old.

假設二元反應曲線為簡單線性羅吉斯迴歸模型(Simple Linear Logistic Regression Model)，在樣本數為偶數的前題下，所謂的最佳D型設計(D-Optimal Design)是直接將半數的樣本點配置在第17.6個百分位數，而另一半則配置在第82.4個百分位數。很遺憾的是，這兩個位置在參數未知的情況下是無法決定的，因此逐次實驗設計法(Sequential Experimental Designs)在應用上就有其必要性。在大樣本的情況下，本文所探討的逐次實驗設計法在理論上具有良好的漸近最佳D型性質(Asymptotic D-Optimality)。尤其重要的是，這些特性並不會因為起始階段的配置不盡理想而消失，影響的只是收斂的快慢而已。但是在實際應用上，這些大樣本的理想性質卻不是我們關注的焦點。實驗步驟收斂速度的快慢，在小樣本的考慮下有決定性的重要性。基於這樣的考量，本文將提出三種起始階段設計的方法並透過模擬比較它們之間的優劣性。 / The D-optimal design is well known to be a two-point design for the simple linear logistic regression function model. Specif-ically , one half of the design points are allocated at the 17.6- th percentile, and the other half at the 82.4-th percentile. Since the locations of the two design points depend on the unknown parameters, the actual 2-locations can not be obtained. In order to dilemma, a sequential design is somehow necessary in practice. Sequential designs disscused in this context have some good properties that would not disappear even the initial stgae is not good enough under large sample size. The speed of converges of the sequential designs is influenced by the initial stage imposed under small sample size. Based on this, three initial stages will be provided in this study and will be compared through simulation conducted by C++ language.

In this work we address to the problems of the loan origination decision-making systems. In accordance with the basic principles of the loan origination process we considered the main rules of a clients parameters estimation, a change-point problem for the given data and a disorder moment detection problem for the real-time observations. In the first part of the work the main principles of the parameters estimation are given. Also the change-point problem is considered for the given sample in the discrete and continuous time with using the Maximum likelihood method. In the second part of the work the disorder moment detection problem for the real-time observations is considered as a disorder problem for a non-homogeneous Poisson process. The corresponding optimal stopping problem is reduced to the free-boundary problem with a complete analytical solution for the case when the intensity of defaults increases. Thereafter a scheme of the real time detection of a disorder moment is given.

Use of geosynthetics for reinforcing soil beds supporting shallow foundations has gained tremendous popularity in recent times. In this thesis, to study and understand the behaviour of geosynthetics reinforced soil foundations, model load tests are carried out on square footings resting on sand beds reinforced with geosynthetics. The effects of various parameters like type and tensile strength of geosynthetic material, depth of reinforced zone, spacing of reinforcement layers, width of reinforcement and form of reinforcement on the performance of square footings on reinforced sand beds are studied. Results from these tests are analyzed to understand the effect of various parameters in improving the bearing capacity and reducing the settlement of footings. An equation is developed to estimate the ultimate bearing capacity of square footings resting on geosynthetic reinforced sand beds by multiple regression analysis of the experimental data. The model loading tests on reinforced soil foundations are simulated in the numerical model using the computer program FLAC3D (Fast Lagrangian Analysis of Continua in 3D). Finally parametric studies on a full scale reinforced soil foundation are conducted. From the experimental, analytical and numerical investigations carried out in this thesis, some important conclusions are drawn regarding the effective depth of reinforced zone, optimum spacing and quantity of reinforcement layers. Relative efficiency of various forms of reinforcement is discussed. Validity of the regression and numerical models developed is verified through experimental data from present study and also for data from other researchers.

目前對於女性董事與財務績效的研究，以國外的文獻居多，台灣的研究僅有3篇，皆是以複迴歸的方式來進行研究分析，僅有一篇是針對金融業進行研究。未來全球經濟的發展，將隨著女性職場上參與程度和社經能力的提升而有所改變。而銀行業對經濟發展有密不可分的關係，故本研究以臺灣21間上市銀行2006年至2011年間的追蹤資料（即126筆樣本觀察值）來進行實證研究。採資料包絡分析法評估績效後，再以Tobit迴歸模型探討女性董監事的比例對台灣上市銀行經營績效的影響，並加入可能影響銀行經營績效的因素作為解釋變數，包括：資本適足率、逾放比率、銀行規模、政府持股比率、銀行是否加入金控、以及時間變數等因素。實證結果發現，女性董事的比例對於銀行的經營績效在統計上有負向的影響，而女性監察人的比例則沒有顯著的影響。建議末來可繼續研究女性董監事與其它產業的績效關係，以增加女性董監事與台灣產業間之關聯性的研究。 / At present, the studies of female directors and financial performances are mostly in foreign documents, and there are only three studies from Taiwan which are based on multiple regression analysis approach of research; only one of those studies focus on financial industry. The global economic development in the future will be changed along with the level of female participation in the workplace and the enhancement of their socio-economic capabilities. Moreover, banking has a close and tight relationship with economic developments. Therefore, this study was based on the traceable data of 21 listed banks in Taiwan from 2006 to 2011 (i.e., 126 sample observations) to proceed the empirical research. After adopting data envelopment analysis to evaluate the performances, it used tobit censored regression model to discuss the influence of the operational performance of listed banks in Taiwan along with the proportion of female directors and supervisors. It also added the possible factors that may affect the banks’ performance as explanatory variables including Capital Adequacy ratio, Non-Performing Loans ratio, size of banks, Public Shareholding ratio, joining in financial holding, time variables and other factors. The result of the study showed that statistically there is a negative effect to the operational performance of the banks along with the proportion of female directors; however, there is no significant impact affected by the proportion of female supervisors. In the future it suggested that the study can be continued researching about the influence of the operational performances in other industries by the proportion of female directors and supervisors in order to increase the research of the correlation between female directors/supervisors and industry performance in Taiwan.

There has been increasing interest in evaluating the performance of existing reinforced concrete (RC) bridges just after natural disasters or man-made events especially when the defects are invisible, or in quantifying the improvement after rehabilitations. In order to obtain an accurate assessment of the reliability of a RC bridge, it is critical to incorporate information about its current structural properties, which reflects the possible aging and deterioration. This dissertation proposes to develop an adaptive reliability analysis of RC bridges incorporating the damage detection information obtained from nondestructive testing (NDT). In this study, seismic fragility is used to describe the reliability of a structure withstanding future seismic demand. It is defined as the conditional probability that a seismic demand quantity attains or exceeds a specified capacity level for given values of earthquake intensity. The dissertation first develops a probabilistic capacity model for RC columns and the capacity model can be used when the flexural stiffness decays nonuniformly over a column height. Then, a general methodology to construct probabilistic seismic demand models for RC highway bridges with one single-column bent is presented. Next, a combination of global and local NDT methods is proposed to identify in-place structural properties. The global NDT uses the dynamic responses of a structure to assess its global/equivalent structural properties and detect potential damage locations. The local NDT uses local measurements to identify the local characteristics of the structure. Measurement and modeling errors are considered in the application of the NDT methods and the analysis of the NDT data. Then, the information obtained from NDT is used in the probabilistic capacity and demand models to estimate the seismic fragility of the bridge. As an illustration, the proposed probabilistic framework is applied to a reinforced concrete bridge with a one-column bent. The result of the illustration shows that the proposed framework can successfully provide the up-to-date structural properties and accurate fragility estimates.