Doctor of Philosophy / Department of Agricultural Economics / Vincent R. Amanor-Boadu / This dissertation is composed of two essays in applied microeconomics. Using farm level data, the first essay applied nonparametric methods to test the adherence of individual farm’s production choices to profit maximization objective. Results indicate that none of the farms consistently satisfy the joint hypothesis of profit maximization. The study took into account the uncertainty prevalent in agricultural production by systematically modeling the optimization behavior of farms. Departures of observed data of individual farms from profit maximization objectives were attributed more due to stochastic influences caused by output production decisions than input use decisions. Results also support the existence of technological progress during the study period for Kansas farms. At an alpha level of 5%, assuming both input and output quantities as stochastic, only 5.3% of the farms violated the joint hypothesis of profit maximization with standard error exceeding 10%. Whereas when only input quantities are considered stochastic, a total of 71.73% and 2.09% of the farms had minimum standard errors of greater than 10% and 20% respectively required for the joint profit maximization hypothesis to hold. When only output quantity measurements were assumed as stochastic, a total of 80.10 % and 18.84 % of the farms had minimum standard errors of greater than 10% and 20% respectively required for the profit maximization hypothesis to hold. The second essay examines the demand for alcoholic beverages (beer, wine and distilled spirits) for the U.S. using time series data from 1979-2006. The estimation is done using an error correction form of the Almost Ideal Demand System . Results indicate that there is a significant difference between short run and long run elasticity estimates. The paper addresses the exogeneity of log of prices and log of real expenditures. For the beer and wine equations, the hypothesis of joint exogeneity of price index and real expenditure cannot be rejected at all the conventional levels of significance. For the spirits equation, the tests strongly reject the simultaneous exogeneity of price index and real expenditure. When independently tested, price index appears to be endogenous variable where as real expenditure seems exogenous variable. Based on these results, the real expenditure was considered as an exogenous variable, where as the price index for spirits as an endogenous variable.

In this thesis appropriate statistical methods to overcome two types of problems that occur during parameter estimation in chemical engineering systems are studied. The first problem is having too many parameters to estimate from limited available data, assuming that the model structure is correct, while the second problem involves estimating unmeasured disturbances, assuming that enough data are available for parameter estimation. In the first part of this thesis, a model is developed to predict rates of undesirable reactions during the finishing stage of nylon 66 production. This model has too many parameters to estimate (56 unknown parameters) and not having enough data to reliably estimating all of the parameters. Statistical techniques are used to determine that 43 of 56 parameters should be estimated. The proposed model matches the data well. In the second part of this thesis, techniques are proposed for estimating parameters in Stochastic Differential Equations (SDEs). SDEs are fundamental dynamic models that take into account process disturbances and model mismatch. Three new approximate maximum likelihood methods are developed for estimating parameters in SDE models. First, an Approximate Expectation Maximization (AEM) algorithm is developed for estimating model parameters and process disturbance intensities when measurement noise variance is known. Then, a Fully-Laplace Approximation Expectation Maximization (FLAEM) algorithm is proposed for simultaneous estimation of model parameters, process disturbance intensities and measurement noise variances in nonlinear SDEs. Finally, a Laplace Approximation Maximum Likelihood Estimation (LAMLE) algorithm is developed for estimating measurement noise variances along with model parameters and disturbance intensities in nonlinear SDEs. The effectiveness of the proposed algorithms is compared with a maximum-likelihood based method. For the CSTR examples studied, the proposed algorithms provide more accurate estimates for the parameters. Additionally, it is shown that the performance of LAMLE is superior to the performance of FLAEM. SDE models and associated parameter estimates obtained using the proposed techniques will help engineers who implement on-line state estimation and process monitoring schemes. / Thesis (Ph.D, Chemical Engineering) -- Queen's University, 2013-12-23 15:12:35.738

The present paper explores the effects of championships won and financial stability of the clubs on share price returns for publicly traded football clubs in Europe. The study uses samples from 2012-2017 of 14 publicly traded clubs on different exchanges such as Borsa Italiana, London Stock Exchange, New York Stock Exchange, Germany Stock Exchange, Paris CAC Index, Borsa Lisbon, Copenhagen Stock Exchange as well as the Turkish Stock Exchange. The initial analysis assesses share price returns’ links with team performance and team financial variables as well as two indices: STOXX 600 Market Index and the STOXX Football Index. Further analysis includes looking at revenues and the different variables that affect returns to see the correlation and understand profitability vs win maximization due to the effect of sugar daddy owners. There appears to be a negative and significant correlation between profit margin and returns, and I also conduct event studies for the biggest transfers of the clubs and conclude that in the short term, there is a significant effect on share prices when transfers occur.

Data dissemination problem is a challenging issue in social networks, especially in mobile social networks, which grows rapidly in recent years worldwide with a significant increasing number of hand-on mobile devices such as smart phones and pads. Short-range radio communications equipped in mobile devices enable mobile users to access their interested contents not only from access points of Internet but also from other mobile users. Through proper data dissemination among mobile users, the bandwidth of the short-range communications can be better utilized and alleviate the stress on the bandwidth of the cellular networks. In this dissertation proposal, data dissemination problem in mobile social networks is studied. Before data dissemination emerges in the research of mobile social networks, routing protocol of finding efficient routing path in mobile social networks was the focus, which later became the pavement for the study of the efficient data dissemination. Data dissemination priorities on packet dissemination from multiple sources to multiple destinations while routing protocol simply focus on finding routing path between two ends in the networks. The first works in the literature of data dissemination problem were based on the modification and improvement of routing protocols in mobile social networks. Therefore, we first studied and proposed a prediction-based routing protocol in delay tolerant networks. Delay tolerant network appears earlier than mobile social networks. With respect to delay tolerant networks, mobile social networks also consider social patterns as well as mobility patterns. In our work, we simply come up with the prediction-based routing protocol through analysis of user mobility patterns. We can also apply our proposed protocol in mobile social networks. Secondly, in literature, efficient data dissemination schemes are proposed to improve the data dissemination ratio and with reasonable overhead in the networks. However, the overhead may be not well controlled in the existing works. A social-aware data dissemination scheme is proposed in this dissertation proposal to study efficient data dissemination problem with controlled overhead in mobile social networks. The data dissemination scheme is based on the study on both mobility patterns and social patterns of mobile social networks. Thirdly, in real world cases, an efficient data dissemination in mobile social networks can never be realized if mobile users are selfish, which is true unfortunately in fact. Therefore, how to strengthen nodal cooperation for data dissemination is studied and a credit-based incentive data dissemination protocol is also proposed in this dissertation. Data dissemination problem was primarily researched on mobile social networks. When consider large social networks like online social networks, another similar problem was researched, namely, information diffusion problem. One specific problem is influence maximization problem in online social networks, which maximize the result of information diffusion process. In this dissertation proposal, we proposed a new information diffusion model, namely, sustaining cascading (SC) model to study the influence maximization problem and based on the SC model, we further plan our research work on the information diffusion problem aiming at minimizing the influence diffusion time with subject to an estimated influence coverage.

Recently numerous emerging applications, ranging from on-line financial transactions, RFID based supply chain management, traffic monitoring to real-time object monitoring, generate high-volume event streams. To meet the needs of processing event data streams in real-time, Complex Event Processing technology (CEP) has been developed with the focus on detecting occurrences of particular composite patterns of events. By analyzing and constructing several real-world CEP applications, we found that CEP needs to be extended with advanced services beyond detecting pattern queries. We summarize these emerging needs in three orthogonal directions. First, for applications which require access to both streaming and stored data, we need to provide a clear semantics and efficient schedulers in the face of concurrent access and failures. Second, when a CEP system is deployed in a sensitive environment such as health care, we wish to mitigate possible privacy leaks. Third, when input events do not carry the identification of the object being monitored, we need to infer the probabilistic identification of events before feed them to a CEP engine. Therefore this dissertation discusses the construction of a framework for extending CEP to support these critical services. First, existing CEP technology is limited in its capability of reacting to opportunities and risks detected by pattern queries. We propose to tackle this unsolved problem by embedding active rule support within the CEP engine. The main challenge is to handle interactions between queries and reactions to queries in the high-volume stream execution. We hence introduce a novel stream-oriented transactional model along with a family of stream transaction scheduling algorithms that ensure the correctness of concurrent stream execution. And then we demonstrate the proposed technology by applying it to a real-world healthcare system and evaluate the stream transaction scheduling algorithms extensively using real-world workload. Second, we are the first to study the privacy implications of CEP systems. Specifically we consider how to suppress events on a stream to reduce the disclosure of sensitive patterns, while ensuring that nonsensitive patterns continue to be reported by the CEP engine. We formally define the problem of utility-maximizing event suppression for privacy preservation. We then design a suite of real-time solutions that eliminate private pattern matches while maximizing the overall utility. Our first solution optimally solves the problem at the event-type level. The second solution, at event-instance level, further optimizes the event-type level solution by exploiting runtime event distributions using advanced pattern match cardinality estimation techniques. Our experimental evaluation over both real-world and synthetic event streams shows that our algorithms are effective in maximizing utility yet still efficient enough to offer near real time system responsiveness. Third, we observe that in many real-world object monitoring applications where the CEP technology is adopted, not all sensed events carry the identification of the object whose action they report on, so called €œnon-ID-ed€� events. Such non-ID-ed events prevent us from performing object-based analytics, such as tracking, alerting and pattern matching. We propose a probabilistic inference framework to tackle this problem by inferring the missing object identification associated with an event. Specifically, as a foundation we design a time-varying graphic model to capture correspondences between sensed events and objects. Upon this model, we elaborate how to adapt the state-of-the-art Forward-backward inference algorithm to continuously infer probabilistic identifications for non-ID-ed events. More important, we propose a suite of strategies for optimizing the performance of inference. Our experimental results, using large-volume streams of a real-world health care application, demonstrate the accuracy, efficiency, and scalability of the proposed technology.

by Long Mei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 73-77). / Abstracts in English and Chinese. / Acknowledgements --- p.iv / Abstract --- p.v / Table of Contents --- p.vii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Nonlinear Factor Analysis Model --- p.1 / Chapter 1.2 --- Main Objectives --- p.2 / Chapter 1.2.1 --- Investigation of the performance of the ML approach with MCECM algorithm in NFA model --- p.2 / Chapter 1.2.2 --- Investigation of the Robustness of the ML approach with MCECM algorithm --- p.3 / Chapter 1.3 --- Structure of the Thesis --- p.3 / Chapter 2 --- Theoretical Background of the MCECM Algorithm --- p.5 / Chapter 2.1 --- Introduction of the EM algorithm --- p.5 / Chapter 2.2 --- Monte Carlo integration --- p.7 / Chapter 2.3 --- Markov Chains --- p.7 / Chapter 2.4 --- The Metropolis-Hastings algorithm --- p.8 / Chapter 3 --- Maximum Likelihood Estimation of a Nonlinear Factor Analysis Model --- p.10 / Chapter 3.1 --- MCECM Algorithm --- p.10 / Chapter 3.1.1 --- Motivation of Using MCECM algorithm --- p.11 / Chapter 3.1.2 --- Introduction of the Realization of the MCECM algorithm --- p.12 / Chapter 3.1.3 --- Implementation of the E-step via the MH Algorithm --- p.13 / Chapter 3.1.4 --- Maximization Step --- p.15 / Chapter 3.2 --- Monitoring Convergence of MCECM --- p.17 / Chapter 3.2.1 --- Bridge Sampling Method --- p.17 / Chapter 3.2.2 --- Average Batch Mean Method --- p.18 / Chapter 4 --- Simulation Studies --- p.20 / Chapter 4.1 --- The First Simulation Study with the Normal Distribution --- p.20 / Chapter 4.1.1 --- Model Specification --- p.20 / Chapter 4.1.2 --- The Selection of System Parameters --- p.22 / Chapter 4.1.3 --- Monitoring the Convergence --- p.22 / Chapter 4.1.4 --- Simulation Results for the ML Estimates --- p.25 / Chapter 4.2 --- The Second Simulation Study with the Normal Distribution --- p.34 / Chapter 4.2.1 --- Model Specification --- p.34 / Chapter 4.2.2 --- Monitoring the Convergence --- p.35 / Chapter 4.2.3 --- Simulation Results for the ML Estimates --- p.38 / Chapter 4.3 --- The Third Simulation Study on Robustness --- p.47 / Chapter 4.3.1 --- Model Specification --- p.47 / Chapter 4.3.2 --- Monitoring the Convergence --- p.48 / Chapter 4.3.3 --- Simulation Results for the ML Estimates --- p.51 / Chapter 4.4 --- The Fourth Simulation Study on Robustness --- p.59 / Chapter 4.4.1 --- Model Specification --- p.59 / Chapter 4.4.2 --- Monitoring the Convergence --- p.59 / Chapter 4.4.3 --- Simulation Results for the ML Estimates --- p.62 / Chapter 5 --- Conclusion --- p.71 / Bibliography --- p.73

Bin Lu. / "September 2004." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (p. 118-126) / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Este estudo teve como objetivo principal desenvolver um modelo de mensuração do desempenho financeiro e econômico para empresas em rede. A justificativa para tal desenvolvimento foi, justamente, uma lacuna verificada nos textos de organização industrial, nova economia institucional e modelos de mensuração do desempenho de empresas e cadeias de suprimentos. Estas pesquisas, até o momento, não enfatizaram, diretamente, questões de cunho financeiro: faltou discutir a dinâmica da variável dinheiro nos modelos. Seguindo este argumento, foi desenvolvido um modelo matemático para otimização do lucro e do EVA (Economic Value Added) levando-se em consideração, além do custo e receita operacional, os custos e receitas financeiras, o tipo de cadeia que a empresa está inserida (atividade), o tipo de estrutura de governança (mercado, rede ou hierarquia) adotado e o seu segmento (elo) de atuação dentro da cadeia. Para validar o modelo, foram coletados dados contábeis de 109 empresas do agronegócio brasileiro, entre os exercícios de 2001 a 2005. Aplicou-se um teste MANOVA (ANOVA Multivariado) para verificar a interferência dos fatores (segmento, cadeia, estrutura e constituição jurídica) sobre a variação dos valores dos indicadores de desempenho financeiro (margem bruta, relação entre exigível de longo prazo sobre patrimônio líquido, retorno sobre ativos e sobre o patrimônio líquido e ciclo de caixa) e econômico (EVA). Pelos resultados, pode-se concluir que todos os fatores apresentaram interferência significativa na variação dos indicadores financeiros e somente o fator segmento interferiu no EVA das empresas. / The aim of this study was to develop an economic and financial performance measurement model for companies in network, since there is a gap in the literature texts of industrial organization, new institutional economy and models of performance measurements of companies and supply chains. In the related literature, these researches did not emphasize the questions related to financial matter, in a direct way, since there is a lack of discussion concerning to the dynamics of the \"money\" in the models. Therefore, a mathematical model was developed with the purpose of maximization of the profit and EVA (Economic Value Added) with emphasis in the financial cost and financial incomes. Moreover, the kind of the company\'s supply chain (business), governance\'s form (market, network or hierarchy) and its segment (actor) in the supply chain was studied. For this purpose, 109 Brazilian agribusiness companies had their accounting and financial data collected, during the period of 2001 and 2005. The statistical test MANOVA was used to detect the interference of the factors (segment, network, governance and legal nature) regarding the economic (EVA) and financial performance drivers range (gross margin, long term liability/net assets, return on assets (ROA) and return on net assets). Within the limits of the present study, we may conclude that all the factors provide significant (a<=0.05) interference in the range of the financial performance drivers. In addition, regarding to the economic performance, the segment was the factor that presented significant differences (a<=0.05), affecting the EVA of the companies.

Melioration can be a factor contributing to behavioral addiction. In this study, 76 university undergraduates operated a "money machine" by selecting between choices that corresponded to maximization and melioration. Participants initially made choices consistent with a strategy of melioration and demonstrated significantly greater variability in choice behavior when visual cues were presented aimed at exposing the internality (or consequence) of the choice situation. Removal of the visual cues resulted in a return to lower responding. Visual cues may aid in interrupting the behavioral addiction pattern by limiting exclusive use of a melioration choice strategy. Methods of restructuring and experimentation with choice allocations are suggested as possible alternatives to melioration.

Learning the parts of objects have drawn more attentions in computer science recently, and they have been playing the important role in computer applications such as object recognition, self-driving cars, and image processing, etc… However, the existing research such as traditional non-negative matrix factorization (NMF), principal component analysis (PCA), and vector quantitation (VQ) has not been discovering the ground-truth bases which are basic components representing objects. On this thesis, I am proposed to study on pattern recognition enhancement combined non-negative matrix factorization (NMF) with automatic relevance determination (ARD). The main point of this research is to propose a new technique combining the algorithm Expectation Maximization (EM) with Automatic Relevance Determination (ARD) to discover the ground truth basis of datasets, and then to compare my new proposed technique to the others such as: traditional NMF, sparseness constraint and graph embedding in pattern recognition problems to verify if my method has over performance in accuracy rate than the others. Particularly, the new technique will be tested on variety of datasets from simple to complex one, from synthetic datasets to real ones. To compare the performance, I split these datasets into 10 random partitions as the training and the testing sets called 10-fold cross validation, and then use the technique called Euclidean algorithm to classify them and test their accuracy. As the result, my proposed method has higher accuracy than the others, and it is good to use in pattern recognition problems with missing data.