Stormwater sewer infrastructure is at risk due to ageing, structural deterioration, population growth, and climate change. Since the consequences of the sewer system failure can adversely impact the community safety, environment and economy, a resilient infrastructure system is of essential importance. However, limited reinvestment budget and insufficient asset management practices impact the rehabilitation of urban sewerage systems. Therefore, an effective and efficient rehabilitation plan is needed to help proper investment decisions. An effective rehabilitation plan will maximize hydraulic performance while minimizing the overall failure risk within a limited budget. The current study aims to address this issue through designing a risk-informed methodology in three steps. First, the hydraulic risk index (obtained using the SWMM model) was combined with the ageing pipe index. The framework uses multi-objective optimization technique to generate solutions under specific sewerage conditions. We named this new framework as Hydraulics and Risk Combined Model (HRCM). Several scenarios including high hydraulic risk, high ageing risk, hydraulic risk and ageing risk (combined problems), and limited budget problems, are used to test the performance of the proposed methodology. The results show that the proposed model could provide a satisfactory solution. Then, in order to increase the calculation speed and improve the accuracy, sensitivity and cost-effectiveness analyses were also conducted for the proposed methodology with different algorithms. The results show that different algorithms offer various benefits. A new calculation method was offered by combining the advantages of the previous methods. Finally, a new optimization method named Phenotype Searching Method, which was enlightened by sexual selection processes, was offered. This method can enhance the selection processes to specific phenotypes (pipes) so that it can increase the convergence speed and increase the performance of the HRCM model.

In this work we explored and proposed flexible models to analyze hierarchical and overdispersed data in agriculture. A semi-parametric generalized linear mixed model was applied and compared with the main standard models to assess count data and, a combined model that take into account overdispersion and clustering through two separate sets of random effects was proposed to model nominal outcomes. For all models, the computational codes were implemented using the SAS software and are available in the appendix. / Nesse trabalho, exploramos e propusemos modelos flexíveis para a análise de dados hierárquicos e superdispersos na agricultura. Um modelo linear generalizado semi- paramétrico misto foi aplicado e comparado com os principais modelos para a análise de dados de contagem e, um modelo combinado que leva em consideração a superdispersão e a hierarquia dos dados por meio de dois efeitos aleatórios distintos foi proposto para a análise de dados nominais. Todos os códigos computacionais foram implementados no software SAS sendo disponibilizados no apêndice.

In this work, we study the so-called combined models, generalized linear mixed models with extension to allow for overdispersion, in the context of genetics and breeding. Such flexible models accommodates cluster-induced correlation and overdispersion through two separate sets of random effects and contain as special cases the generalized linear mixed models (GLMM) on the one hand, and commonly known overdispersion models on the other. We use such models while obtaining heritability coefficients for non-Gaussian characters. Heritability is one of the many important concepts that are often quantified upon fitting a model to hierarchical data. It is often of importance in plant and animal breeding. Knowledge of this attribute is useful to quantify the magnitude of improvement in the population. For data where linear models can be used, this attribute is conveniently defined as a ratio of variance components. Matters are less simple for non-Gaussian outcomes. The focus is on time-to-event and count traits, where the Weibull-Gamma-Normal and Poisson-Gamma-Normal models are used. The resulting expressions are sufficiently simple and appealing, in particular in special cases, to be of practical value. The proposed methodologies are illustrated using data from animal and plant breeding. Furthermore, attention is given to the occurrence of negative estimates of variance components in the Poisson-Gamma-Normal model. The occurrence of negative variance components in linear mixed models (LMM) has received a certain amount of attention in the literature whereas almost no work has been done for GLMM. This phenomenon can be confusing at first sight because, by definition, variances themselves are non-negative quantities. However, this is a well understood phenomenon in the context of linear mixed modeling, where one will have to make a choice between a hierarchical and a marginal view. The variance components of the combined model for count outcomes are studied theoretically and the plant breeding study used as illustration underscores that this phenomenon can be common in applied research. We also call attention to the performance of different estimation methods, because not all available methods are capable of extending the parameter space of the variance components. Then, when there is a need for inference on such components and they are expected to be negative, the accuracy of the method is not the only characteristic to be considered. / Neste trabalho foram estudados os chamados modelos combinados, modelos lineares generalizados mistos com extensão para acomodar superdispersão, no contexto de genética e melhoramento. Esses modelos flexíveis acomodam correlação induzida por agrupamento e superdispersão por meio de dois conjuntos separados de efeitos aleatórios e contem como casos especiais os modelos lineares generalizados mistos (MLGM) e os modelos de superdispersão comumente conhecidos. Tais modelos são usados na obtenção do coeficiente de herdabilidade para caracteres não Gaussianos. Herdabilidade é um dos vários importantes conceitos que são frequentemente quantificados com o ajuste de um modelo a dados hierárquicos. Ela é usualmente importante no melhoramento vegetal e animal. Conhecer esse atributo é útil para quantificar a magnitude do ganho na população. Para dados em que modelos lineares podem ser usados, esse atributo é convenientemente definido como uma razão de componentes de variância. Os problemas são menos simples para respostas não Gaussianas. O foco aqui é em características do tipo tempo-até-evento e contagem, em que os modelosWeibull-Gama-Normal e Poisson-Gama-Normal são usados. As expressões resultantes são suficientemente simples e atrativas, em particular nos casos especiais, pelo valor prático. As metodologias propostas são ilustradas usando dados de melhoramento animal e vegetal. Além disso, a atenção é voltada à ocorrência de estimativas negativas de componentes de variância no modelo Poisson-Gama- Normal. A ocorrência de componentes de variância negativos em modelos lineares mistos (MLM) tem recebido certa atenção na literatura enquanto quase nenhum trabalho tem sido feito para MLGM. Esse fenômeno pode ser confuso a princípio porque, por definição, variâncias são quantidades não-negativas. Entretanto, este é um fenômeno bem compreendido no contexto de modelagem linear mista, em que a escolha deverá ser feita entre uma interpretação hierárquica ou marginal. Os componentes de variância do modelo combinado para respostas de contagem são estudados teoricamente e o estudo de melhoramento vegetal usado como ilustração confirma que esse fenômeno pode ser comum em pesquisas aplicadas. A atenção também é voltada ao desempenho de diferentes métodos de estimação, porque nem todos aqueles disponíveis são capazes de estender o espaço paramétrico dos componentes de variância. Então, quando há a necessidade de inferência de tais componentes e é esperado que eles sejam negativos, a acurácia do método de estimação não é a única característica a ser considerada.

In this work, we study the so-called combined models, generalized linear mixed models with extension to allow for overdispersion, in the context of genetics and breeding. Such flexible models accommodates cluster-induced correlation and overdispersion through two separate sets of random effects and contain as special cases the generalized linear mixed models (GLMM) on the one hand, and commonly known overdispersion models on the other. We use such models while obtaining heritability coefficients for non-Gaussian characters. Heritability is one of the many important concepts that are often quantified upon fitting a model to hierarchical data. It is often of importance in plant and animal breeding. Knowledge of this attribute is useful to quantify the magnitude of improvement in the population. For data where linear models can be used, this attribute is conveniently defined as a ratio of variance components. Matters are less simple for non-Gaussian outcomes. The focus is on time-to-event and count traits, where the Weibull-Gamma-Normal and Poisson-Gamma-Normal models are used. The resulting expressions are sufficiently simple and appealing, in particular in special cases, to be of practical value. The proposed methodologies are illustrated using data from animal and plant breeding. Furthermore, attention is given to the occurrence of negative estimates of variance components in the Poisson-Gamma-Normal model. The occurrence of negative variance components in linear mixed models (LMM) has received a certain amount of attention in the literature whereas almost no work has been done for GLMM. This phenomenon can be confusing at first sight because, by definition, variances themselves are non-negative quantities. However, this is a well understood phenomenon in the context of linear mixed modeling, where one will have to make a choice between a hierarchical and a marginal view. The variance components of the combined model for count outcomes are studied theoretically and the plant breeding study used as illustration underscores that this phenomenon can be common in applied research. We also call attention to the performance of different estimation methods, because not all available methods are capable of extending the parameter space of the variance components. Then, when there is a need for inference on such components and they are expected to be negative, the accuracy of the method is not the only characteristic to be considered. / Neste trabalho foram estudados os chamados modelos combinados, modelos lineares generalizados mistos com extensão para acomodar superdispersão, no contexto de genética e melhoramento. Esses modelos flexíveis acomodam correlação induzida por agrupamento e superdispersão por meio de dois conjuntos separados de efeitos aleatórios e contem como casos especiais os modelos lineares generalizados mistos (MLGM) e os modelos de superdispersão comumente conhecidos. Tais modelos são usados na obtenção do coeficiente de herdabilidade para caracteres não Gaussianos. Herdabilidade é um dos vários importantes conceitos que são frequentemente quantificados com o ajuste de um modelo a dados hierárquicos. Ela é usualmente importante no melhoramento vegetal e animal. Conhecer esse atributo é útil para quantificar a magnitude do ganho na população. Para dados em que modelos lineares podem ser usados, esse atributo é convenientemente definido como uma razão de componentes de variância. Os problemas são menos simples para respostas não Gaussianas. O foco aqui é em características do tipo tempo-até-evento e contagem, em que os modelosWeibull-Gama-Normal e Poisson-Gama-Normal são usados. As expressões resultantes são suficientemente simples e atrativas, em particular nos casos especiais, pelo valor prático. As metodologias propostas são ilustradas usando dados de melhoramento animal e vegetal. Além disso, a atenção é voltada à ocorrência de estimativas negativas de componentes de variância no modelo Poisson-Gama- Normal. A ocorrência de componentes de variância negativos em modelos lineares mistos (MLM) tem recebido certa atenção na literatura enquanto quase nenhum trabalho tem sido feito para MLGM. Esse fenômeno pode ser confuso a princípio porque, por definição, variâncias são quantidades não-negativas. Entretanto, este é um fenômeno bem compreendido no contexto de modelagem linear mista, em que a escolha deverá ser feita entre uma interpretação hierárquica ou marginal. Os componentes de variância do modelo combinado para respostas de contagem são estudados teoricamente e o estudo de melhoramento vegetal usado como ilustração confirma que esse fenômeno pode ser comum em pesquisas aplicadas. A atenção também é voltada ao desempenho de diferentes métodos de estimação, porque nem todos aqueles disponíveis são capazes de estender o espaço paramétrico dos componentes de variância. Então, quando há a necessidade de inferência de tais componentes e é esperado que eles sejam negativos, a acurácia do método de estimação não é a única característica a ser considerada.

近年來，在中國大陸市場有大量公司進行掛牌上市的同時，也有越來越多的公司出現債務逾期甚至是違約的情況。考慮到目前中國經濟增速放緩，處在轉型發展的複雜階段，銀行信貸等資金供應鏈需要謹慎評估企業出現財務困境的風險。但是我們發現金融機構在平常管理信貸業務的時候會盲目地看重高額利潤的回報而忽略借款者潛在的財務危機，而且投資人在進行投資分析的時候往往也會忽略企業的財務狀況而使自己遭受損失，因此從企業的財務狀況入手對其進行財務困境機率的評估有非常重大的現實意義。 本文通過對企業財務指標進行相關分析以構建公司財務困境機率評估模型。本文選取了不良貸款率最高的製造業作為研究對象，將2015年滬深兩地的124家上市製造業公司的財務資料作為訓練樣本，將2014年120家上市公司的財務資料作為檢驗樣本，將交易所特別處理公司劃分為非正常組公司，其餘為正常組。本文通過篩選得出23個財務指標作為研究變數，引入了 Logistic 模型與 SVM 模型，針對單一模型的預測結果在準確率和穩定性方面不理想的問題引入了基於 Logistic 模型、SVM 模型的組合模型，並用檢驗樣本進行了四個模型的相關實證分析，比較了四個模型之間的準確度。 對四個模型進行實證分析的結果表明：Logistic模型穩健性好、可解釋性強、建模過程簡單易操作，但分類精度略低於 SVM 模型；SVM雖然分類精度高，但缺乏可解釋性和穩定性，且建模過程依賴專家知識和經驗；Logistic -SVM 組合模型則兼具其優點，預測精確度較單一模型均有提高，而且研究發現異態並行結構優於串型結構。通過本文建立的模型可以計算出企業的陷入財務困境的機率，有效評估企業的違約風險，進而為相關金融機構和投資者提供放款或投資的判斷依據。 / At present, more and more companies are listed in the Chinese mainland market. At the same time, more and more companies are frequently at risk of default and overdue. Given the slowdown in China's economic growth and the complex environment of transformation and development, the supply of funds such as bank loans and other capital needs to be cautious, debt default, loan overdue cases are still likely to occur one after another. However, we find that financial institutions blindly value the return of high profits while ignoring the potential financial crisis of borrowers in the normal management of credit business, it is of great significance to start with the financial status of a company to assess the probability of financial distress. This paper builds a company default probability assessment model by analyzing the financial indicators of enterprises. This paper selects the manufacturing industry with the highest NPL as the research object. Taking the financial data of 124 listed manufacturing companies in Shanghai and Shenzhen in 2015 as the training samples, using the financial data of 120 listed companies in 2014 as the test sample, Exchange special treatment companies divided into non-normal group companies, the rest for the normal group. According to the data of its 2015 financial indicators, 23 financial indicators were screened out as research variables, and a comprehensive analysis was carried out. The Logistic model and SVM model were introduced. Combined model was introduced based on the Logistic model and SVM model to solve the problem that the prediction accuracy and stability of the single model were not ideal,. Finally, empirical analysis of the four models is carried out using the sample data of listed companies in 2014, and the accuracy of the four models is compared. The results of empirical analysis of the four models show that Logistic regression model has no strict assumptions on the data, a better stability and interpretation, but the classification accuracy is slightly lower than the SVM model. The SVM model has higher classification accuracy, but the disadvantage is the lack of interpretability and stability, the modeling process depends on expert knowledge and experience. In order to balance the stability of Logistic model and the accuracy of SVM model, this paper introduces a combined model based on Logistic model and SVM model. The analysis shows that the prediction accuracy of combined model is higher than that of single model, the combination of Logistic regression model and SVM model based on Parallel structure has a higher prediction accuracy than Sequential structure. The model established in this paper can calculate the default probability of an enterprise, effectively assess companies’ risk of financial distress, and then provide the judgment basis for the relevant financial institutions and investors to lend or invest.

企業評價對於投資決策有重大的影響，不論是發行上市、或是機構投資人選擇投資標的、乃至於併購或是清算，企業評價都是一切的基礎。再加上近來各界對於節能產業的重視，發光二極體封裝產業正如日中天的高度成長，如何能夠正確地衡量此產業的企業價值，實是機構投資人或是一般大眾關心的課題。此外，實務界長久以來詬病證管會所採用的承銷價格公式，乃是結合不同評價模式的方式來評斷發行股票公司之正確股票價值，但實證結果往往發現此公式會造成股價被低估的現象。 本研究以台灣地區共六家發光二極體封裝產業上市櫃公司為例，以其民國八十七年至九十四年的財務數據和資料，以五年為一階段，利用七種不同的評價模式：三階段成長現金流量折現法、三階段成長本益比法、三階段成長股價淨值比法、三階段股價銷售額比法、市場比較本益比法、市場比較股價淨值比法以及市場比較股價銷售額比法，預期九十二年初至九十五年初之理論實際股價，並與實際的市價作一比較，利用THEIL所提出的THEIL’S U值來比較不同評價模型與實際市價差距的績效，以選出最適合發光二極體封裝產業之企業評價模式。 本研究更進一步探討長久以來被實務界所詬病的綜合評價模式（結合不同的評價模式），試著經過第一階段實證結果的篩選，利用簡單權重結合本產業最佳和次佳的企業評價模式，以得到一個評價績效更勝於最佳評價模式的綜合評價法。 實證結果顯示，發光二極體最佳評價模型乃為市場比較股價銷售額比法(THEIL’S U=0.3515)，而三階段成長現金流量折現法，則適用於產業較穩定的情況下。突破性的發現則為，利用THEIL’S U值來比較評價績效而選出的最佳和次佳模型，在分別給予簡單權重（ex：50％：50％、60％：40％等）的情況下所得到的綜合評價法，其THEIL’S U值（<0.3515）比當初單一最佳評價法--市場比較股價銷售額比法(0.3515)還要來得低，顯示綜合評價法的有效性的確存在，並值得各界參考。此外，亦發現給予最佳評價法較大權重時，更可以進一步提昇綜合評價法之績效。此結果反駁了實務界長久以來對於綜合評價法的不信任，也給予證管會一個修正承銷價格公式的方向。跨類型的評價法結合並不是不可行，但是需要第一階段各個評價法的評價績效驗證，讓較佳的評價模式彼此結合以產生資訊互補的效果。