The colonias along the Texas-Mexico border are one of the most rapidly growing areas in Texas. Because of the relatively low income of the residents and an inadequate availability of transportation services, the need for basic social activities for the colonias cannot be properly met. The objectives of this study are to have a better comprehension of the status quo of these communities by examining the potential demand for an improved transportation service and evaluate the capacity and optimum service time interval of a new demand responsive transit "feeder" service within one representative colonia, El Cenizo. A comprehensive analysis of the results of a survey conducted through a questionnaire is presented to explain the existing travel patterns and potential demand for a feeder service. The results of this thesis and work from the subsequent simulation analysis showed that a single shuttle would be able to comfortably serve 150 passengers/day. It further showed that the optimal cycle length between consecutive departures from the terminal should be between 11-13 minutes for best service quality. This exploratory study should serve as a first step towards improving transportation services within these growing underprivileged communities especially those with demographics and geography similar to the target area of El Cenizo.

Metaphylactic treatment of incoming feedlot cattle is a common preventative action against bovine respiratory disease (BRD). Cattle are dosed based on estimated or actual lot average weights, rather than on an individual basis, to reduce initial processing time. There has been limited research conducted on the effects of accurate weight- based dosing in feedlot cattle. The objective of this study was to evaluate the economic effects of precision weight- based dosing of cattle as compared to dosing the lot average or lot averages plus 50 lb and minus 50 lb. An economic model was created and stochastic simulations performed to evaluate potential outcomes of different dosing scenarios. Economic analyses of the effects of precision weight-based dosing were conducted using SIMETAR© to determine the stochastic dominance and economic effects of different dosing regimens. Data were obtained from a commercial feedlot for different lots of cattle where individual animal weights were available; for this analysis the minimum lot size was 30 animals, and the maximum lot size was 126 animals. Within lots, individual weight deviations were calculated from the lot mean, the lot mean was rounded up to the nearest 50 lb increment or down to the nearest 50 lb increment to represent mild overestimation and mild underestimation, respectively. Tulathromycin (Draxxin®, Pfizer Animal Health, New York, NY), an antimicrobial commonly prescribed for treatment of bovine respiratory disease, was used to illustrate the impacts of uniform dosing versus exact dosing per body weight. Based on the dilution space method used to evaluate time of drug effectiveness, it was estimated that Draxxin® administered at the recommended dosage to cattle weighing between 500 and 1000 lb should be provided with 191 hours (7.96 days) of protection from pneumonia-causing bacteria. Due to the pharmacokinetic properties of Draxxin®, an animal that is administered half the recommended dose is only protected from pneumonia-causing bacteria for 8 hours, which is 4.2 percent of the coverage time of the proper dose. This limits the effectiveness of the prescribed treatment to fully administer therapeutic treatment. In all cases, the correct weight-based dosing strategy cost less than any other dosing technique. Overall, dosing all cattle at the lot average weight costs $6.04 per animal more than dosing at the exact, correct dose. Dosing all animals at the lot average weight plus 50 lb costs $6.24 per animal more; dosing all animals at lot average minus 50 lb costs $4.01 per animal more. The use of individual animal weights to determine per head dosing of Draxxin® is more cost effective than using lot averages. This concept would appear to extend to all weight-based pharmaceutical products in general, and should be considered a necessary management strategy.

Stochastic di®erential equations (SDEs) are　central to much of modern finance theory and have been widely used to model the behaviour of key variables such as the　instantaneous short-term interest rate, asset prices, asset returns and their volatility.　The explanatory and/or predictive power of these models depends crucially on the particularisation of the model SDE(s) to real data through the choice of values for their parameters. In econometrics, optimal parameter estimates are generally considered to　be those that maximise the likelihood of the sample. In the context of the estimation of the parameters of SDEs, however, a closed-form expression for the likelihood　function is rarely available and hence exact maximum-likelihood (EML) estimation is usually infeasible. The key research problem examined in this thesis is the development of generic, accurate and computationally feasible estimation procedures based on the　ML principle, that can be implemented in the absence of a closed-form expression for　the likelihood function. The overall recommendation to come out of the thesis is that　an estimation procedure based on the finite-element solution of a reformulation of the Fokker-Planck equation in terms of the transitional cumulative distribution function(CDF) provides the best balance across all of the desired characteristics. The recommended approach involves the use of an interpolation technique proposed in this thesis which greatly reduces the required computational effort.

Motivated by a study of social spider behavior, we discuss the distribution of the distances from all the events in a spatial point pattern to a point of reference that has a known location at a given moment of time. The distribution depends on both the shape of the region and the location of the point of reference. The empirical CDF is used to describe the distribution of the distances and compare it to the CDF derived under complete spatial randomness. Empirical distributions are then compared through time focusing on the case in which the point of reference changes with time.

Orthogonal frequency division multiplexing (OFDM) system is the standard of next generation mobile communication, one of the major drawbacks of OFDM systems is the peak-to-average power ratio (PAPR). In this paper, we proposed a low complexity Selected mapping (SLM) scheme to reduce PAPR. In [27], Wang proposed a low complexity SLM scheme by utilizing conversion vectors having the form of a perfect sequence to solve the problem that phase rotation vectors of the conversion vectors do not usually have an equal magnitude in frequency domain. This paper proposed a low complexity SLM scheme based on perfect sequence and consider the symbol interleaving to reduce the correlation between signals in time domain. It is shown that the (Complementary Cumulative Distribution Function, CCDF) of our proposed scheme are closer to the traditional SLM scheme than Wang¡¦s in [27] but with additional complexity. And the computational complexity is much lower than traditional SLM.

For acquisition of narrow-beam free-space optical (FSO) terminals, a Global Positioning System (GPS) is typically required for coarse localization of the terminal. However, the GPS signal may be shadowed, or may not be present at all, especially in rough or unnameable terrains. In this study, we propose a lidar-assisted acquisition of an unmanned aerial vehicle (UAV) for FSO communications in a poor GPS environment. Such an acquisition system consists of a lidar subsystem and an FSO acquisition subsystem: The lidar system is used for coarse acquisition of the UAV, whereas, the FSO system is utilized for fine acquisition to obtain the UAV’s accurate position. This study investigates the optimal allocation of energy between the lidar and FSO systems to minimize the acquisition time. Here, we minimize the average acquisition time, and maximize the cumulative distribution function of acquisition time for a fixed threshold. We learn that an optimal value of the energy allocation factor exists that provides the best performance of the proposed system.

This thesis presents a class of graphical models for directly representing the joint cumulative distribution function (CDF) of many random variables, called cumulative distribution networks (CDNs). Unlike graphical models for probability density and mass functions, in a CDN, the marginal probabilities for any subset of variables are obtained by computing limits of functions in the model. We will show that the conditional independence properties in a CDN are distinct from the conditional independence properties of directed, undirected and factor graph models, but include the conditional independence properties of bidirected graphical models. As a result, CDNs are a parameterization for bidirected models that allows us to represent complex statistical dependence relationships between observable variables. We will provide a method for constructing a factor graph model with additional latent variables for which graph separation of variables in the corresponding CDN implies conditional independence of the separated variables in both the CDN and in the factor graph with the latent variables marginalized out. This will then allow us to construct multivariate extreme value distributions for which both a CDN and a corresponding factor graph representation exist. In order to perform inference in such graphs, we describe the `derivative-sum-product' (DSP) message-passing algorithm where messages correspond to derivatives of the joint cumulative distribution function. We will then apply CDNs to the problem of learning to rank, or estimating parametric models for ranking, where CDNs provide a natural means with which to model multivariate probabilities over ordinal variables such as pairwise preferences. We will show that many previous probability models for rank data, such as the Bradley-Terry and Plackett-Luce models, can be viewed as particular types of CDN. Applications of CDNs will be described for the problems of ranking players in multiplayer team-based games, document retrieval and discovering regulatory sequences in computational biology using the above methods for inference and estimation of CDNs.

Os controladores eletrônicos de pulverização visam minimizar a variação das taxas de insumos aplicadas no campo. Eles fazem parte de um sistema de controle, e permitem a compensação da variação de velocidade de deslocamento do pulverizador durante a operação. Há vários tipos de controladores eletrônicos de pulverização disponíveis no mercado e uma das formas de selecionar qual o mais eficiente nas mesmas condições, ou seja, em um mesmo sistema de controle, é quantificar o tempo de resposta do sistema para cada controlador específico. O objetivo desse trabalho foi estimar os tempos de resposta para mudanças de velocidade de um sistema eletrônico de pulverização via modelos de regressão não lineares, estes, resultantes da soma de regressões lineares ponderadas por funções distribuição acumulada. Os dados foram obtidos no Laboratório de Tecnologia de Aplicação, localizado no Departamento de Engenharia de Biossistemas da Escola Superior de Agricultura \"Luiz de Queiroz\", Universidade de São Paulo, no município de Piracicaba, São Paulo, Brasil. Os modelos utilizados foram o logístico e de Gompertz, que resultam de uma soma ponderada de duas regressões lineares constantes com peso dado pela função distribuição acumulada logística e Gumbell, respectivamente. Reparametrizações foram propostas para inclusão do tempo de resposta do sistema de controle nos modelos, com o objetivo de melhorar a interpretação e inferência estatística dos mesmos. Foi proposto também um modelo de regressão não linear difásico que resulta da soma ponderada de regressões lineares constantes com peso dado pela função distribuição acumulada Cauchy seno hiperbólico exponencial. Um estudo de simulação foi feito, utilizando a metodologia de Monte Carlo, para avaliar as estimativas de máxima verossimilhança dos parâmetros do modelo. / The electronic controllers spray aimed at minimizing the variation of inputs rates applied in the field. They are part of a control system, and allow for compensation for variation spray travel speed during operation. There are several types of electronic spray controllers on the market and one way to select which more efficient under the same conditions, ie in the same system of control, is to quantify the system response time for each specific driver. The objective of this study was to estimate the response times for changes in speed of an electronic spraying system via nonlinear regression models, these resulting from the sum of weighted linear regressions for cumulative distribution functions. Data were obtained on the Application Technology Laboratory, located in the Department of Biosystems Engineering from College of Agriculture \"Luiz de Queiroz\", University of Sao Paulo, in Piracicaba, Sao Paulo, Brazil. The models used were the logistic and Gompertz, resulting from a weighted sum of two constant linear regressions with weight given by the cumulative distribution function logistics and Gumbell respectively. Reparametrization been proposed for inclusion in the control system response time models, in order to improve the statistical interpretation and inference of the same. It has also been proposed a non-linear regression model two-phase which is the weighted sum of constant linear regressions weight given by a cumulative distribution function exponential hyperbolic sine Cauchy in which a simulation study was conducted using the methodology of Monte Carlo to evaluating the maximum likelihood estimates of the model parameters.

This thesis presents a class of graphical models for directly representing the joint cumulative distribution function (CDF) of many random variables, called cumulative distribution networks (CDNs). Unlike graphical models for probability density and mass functions, in a CDN, the marginal probabilities for any subset of variables are obtained by computing limits of functions in the model. We will show that the conditional independence properties in a CDN are distinct from the conditional independence properties of directed, undirected and factor graph models, but include the conditional independence properties of bidirected graphical models. As a result, CDNs are a parameterization for bidirected models that allows us to represent complex statistical dependence relationships between observable variables. We will provide a method for constructing a factor graph model with additional latent variables for which graph separation of variables in the corresponding CDN implies conditional independence of the separated variables in both the CDN and in the factor graph with the latent variables marginalized out. This will then allow us to construct multivariate extreme value distributions for which both a CDN and a corresponding factor graph representation exist. In order to perform inference in such graphs, we describe the `derivative-sum-product' (DSP) message-passing algorithm where messages correspond to derivatives of the joint cumulative distribution function. We will then apply CDNs to the problem of learning to rank, or estimating parametric models for ranking, where CDNs provide a natural means with which to model multivariate probabilities over ordinal variables such as pairwise preferences. We will show that many previous probability models for rank data, such as the Bradley-Terry and Plackett-Luce models, can be viewed as particular types of CDN. Applications of CDNs will be described for the problems of ranking players in multiplayer team-based games, document retrieval and discovering regulatory sequences in computational biology using the above methods for inference and estimation of CDNs.

Os controladores eletrônicos de pulverização visam minimizar a variação das taxas de insumos aplicadas no campo. Eles fazem parte de um sistema de controle, e permitem a compensação da variação de velocidade de deslocamento do pulverizador durante a operação. Há vários tipos de controladores eletrônicos de pulverização disponíveis no mercado e uma das formas de selecionar qual o mais eficiente nas mesmas condições, ou seja, em um mesmo sistema de controle, é quantificar o tempo de resposta do sistema para cada controlador específico. O objetivo desse trabalho foi estimar os tempos de resposta para mudanças de velocidade de um sistema eletrônico de pulverização via modelos de regressão não lineares, estes, resultantes da soma de regressões lineares ponderadas por funções distribuição acumulada. Os dados foram obtidos no Laboratório de Tecnologia de Aplicação, localizado no Departamento de Engenharia de Biossistemas da Escola Superior de Agricultura \"Luiz de Queiroz\", Universidade de São Paulo, no município de Piracicaba, São Paulo, Brasil. Os modelos utilizados foram o logístico e de Gompertz, que resultam de uma soma ponderada de duas regressões lineares constantes com peso dado pela função distribuição acumulada logística e Gumbell, respectivamente. Reparametrizações foram propostas para inclusão do tempo de resposta do sistema de controle nos modelos, com o objetivo de melhorar a interpretação e inferência estatística dos mesmos. Foi proposto também um modelo de regressão não linear difásico que resulta da soma ponderada de regressões lineares constantes com peso dado pela função distribuição acumulada Cauchy seno hiperbólico exponencial. Um estudo de simulação foi feito, utilizando a metodologia de Monte Carlo, para avaliar as estimativas de máxima verossimilhança dos parâmetros do modelo. / The electronic controllers spray aimed at minimizing the variation of inputs rates applied in the field. They are part of a control system, and allow for compensation for variation spray travel speed during operation. There are several types of electronic spray controllers on the market and one way to select which more efficient under the same conditions, ie in the same system of control, is to quantify the system response time for each specific driver. The objective of this study was to estimate the response times for changes in speed of an electronic spraying system via nonlinear regression models, these resulting from the sum of weighted linear regressions for cumulative distribution functions. Data were obtained on the Application Technology Laboratory, located in the Department of Biosystems Engineering from College of Agriculture \"Luiz de Queiroz\", University of Sao Paulo, in Piracicaba, Sao Paulo, Brazil. The models used were the logistic and Gompertz, resulting from a weighted sum of two constant linear regressions with weight given by the cumulative distribution function logistics and Gumbell respectively. Reparametrization been proposed for inclusion in the control system response time models, in order to improve the statistical interpretation and inference of the same. It has also been proposed a non-linear regression model two-phase which is the weighted sum of constant linear regressions weight given by a cumulative distribution function exponential hyperbolic sine Cauchy in which a simulation study was conducted using the methodology of Monte Carlo to evaluating the maximum likelihood estimates of the model parameters.