Existe uma grande e crescente literatura sobre especificação e estimação de fronteiras de produção e, portanto, de eficiência de unidades produtivas. Nesta tese, o foco esta sobre modelos de fronteiras determinísticas, os quais são baseados na hipótese de que os dados observados pertencem ao conjunto tecnológico. Dentre os modelos estatísticos e estimadores para fronteiras determinísticas existentes, uma abordagem promissora e a adotada por Martins-Filho e Yao (2007). Esses autores propõem um procedimento de estimação composto por três estágios. Esse estimador e de fácil implementação, visto que envolve procedimentos não-paramétricos bem conhecidos. Além disso, o estimador possui características desejáveis vis-à-vis estimadores para fronteiras determinísticas tradicionais como DEA e FDH. Nesta tese, três artigos, que melhoram o modelo proposto por Martins-Filho e Yao (2007), sao propostos. No primeiro artigo, o procedimento de estimação desses autores e melhorado a partir de uma variação do estimador exponencial local, proposto por Ziegelmann (2002). Demonstra-se que estimador proposto a consistente e assintoticamente normal. Além disso, devido ao estimador exponencial local, estimativas potencialmente negativas para a função de variância condicional, que poderiam prejudicar a aplicabilidade do estimador proposto por Martins-Filho e Yao, são evitadas. No segundo artigo, e proposto um método original para estimação de fronteiras de produção em apenas dois estágios. E mostrado que se pode eliminar o segundo estágio proposto por Martins-Filho e Yao, assim como, eliminar o segundo estagio proposto no primeiro artigo desta tese. Em ambos os casos, a estimação do mesmo modelo de fronteira de produção requer três estágios, sendo versões diferentes para o segundo estagio. As propriedades assintóticas do estimador proposto são analisadas, mostrando-se consistência e normalidade assintótica sob hipóteses razoáveis. No terceiro artigo, a proposta uma variação semi-paramétrica do modelo estudado no segundo artigo. Reescreve-se aquele modelo de modo que se possa estimar a fronteira de produção e a eficiência de unidades produtivas no contexto de múltiplos insumos, sem incorrer no curse of dimensionality. A abordagem adotada coloca o modelo na estrutura de modelos aditivos, a partir de hipóteses sobre como os insumos se combinam no processo produtivo. Em particular, considera-se aqui os casos de insumos aditivos e insumos multiplicativos, os quais são amplamente considerados em teoria econômica e aplicações. Estudos de Monte Carlo são apresentados em todos os artigos, afim de elucidar as propriedades dos estimadores propostos em amostras finitas. Além disso, estudos com dados reais são apresentados em todos os artigos, nos quais são estimador rankings de eficiência para uma amostra de departamentos policiais dos EUA, a partir de dados sobre criminalidade daquele país. / There exists a large and growing literature on the specification and estimation of production frontiers and therefore efficiency of production units. In this thesis we focus on deterministic production frontier models, which are based on the assumption that all observed data lie in the technological set. Among the existing statistical models and estimators for deterministic frontiers, a promising approach is that of Martins-Filho and Yao (2007). They propose an estimation procedure that consists of three stages. Their estimator is fairly easy to implement as it involves standard nonparametric procedures. In addition, it has a number of desirable characteristics vis-a-vis traditional deterministic frontier estimators as DEA and FDH. In this thesis we propose three papers that improve the model proposed in Martins-Filho and Yao (2007). In the first paper we improve their estimation procedure by adopting a variant of the local exponential smoothing proposed in Ziegelmann (2002). Our estimator is shown to be consistent and asymptotically normal. In addition, due to local exponential smoothing, potential negativity of conditional variance functions that may hinder the use of Martins-Filho and Yao's estimator is avoided. In the second paper we propose a novel method for estimating production frontiers in only two stages. (Continue). There we show that we can eliminate the second stage of Martins-Filho and Yao as well as of our first paper, where estimation of the same frontier model requires three stages under different versions for the second stage. We study asymptotic properties showing consistency andNirtnin, asymptotic normality of our proposed estimator under standard assumptions. In the third paper we propose a semiparametric variation of the frontier model studied in the second paper. We rewrite that model allowing for estimating the production frontier and efficiency of production units in a multiple input context without suffering the curse of dimensionality. Our approach places that model within the framework of additive models based on assumptions regarding the way inputs combine in production. In particular, we consider the cases of additive and multiplicative inputs, which are widely considered in economic theory and applications. Monte Carlo studies are performed in all papers to shed light on the finite sample properties of the proposed estimators. Furthermore a real data study is carried out in all papers, from which we rank efficiency within a sample of USA Law Enforcement agencies using USA crime data.

Submitted by Aline Batista (alinehb.ufpel@gmail.com) on 2017-08-16T14:52:32Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Roberta_Machado_Karsburg_Precipitação_e_velocidade_do_vento_na_oscilação_dos_níveis_dagua.pdf: 1951339 bytes, checksum: 21d939a36d26dac261dcd69cef73e42c (MD5) / Made available in DSpace on 2017-08-16T14:52:32Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Roberta_Machado_Karsburg_Precipitação_e_velocidade_do_vento_na_oscilação_dos_níveis_dagua.pdf: 1951339 bytes, checksum: 21d939a36d26dac261dcd69cef73e42c (MD5) Previous issue date: 2016-10-26 / Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul - FAPERGS / O canal São Gonçalo se configura como um importante curso d’água, pertencente a bacia hidrográfica Mirim-São Gonçalo, que tem área de 25.000 km² e situa-se na região costeira no estado do Rio Grande do Sul. Aliado a isso, ele une a laguna dos Patos, a qual mantém conexão direta com o oceano Atlântico, à lagoa Mirim, considerada como um grande reservatório de água doce no sul do Brasil. Está situado em uma região de planície, de baixas declividades e apresenta grande complexidade e sensibilidade às oscilações dos níveis d’água, tanto em função do regime e direção de ventos como do regime de chuvas. Com isso, este trabalho teve como objetivo avaliar a influência do vento e da precipitação na oscilação do nível d`água à jusante da barragem eclusa do canal São Gonçalo-RS. A hipótese foi que a velocidade e direção do vento, juntamente com a precipitação, influenciam à oscilação dos níveis do canal São Gonçalo. Para alcançar os objetivos, foram empregados métodos de regressão linear múltipla e simples entre o nível d’água, precipitação e velocidades do vento à 2 e 7 m de altura e velocidade máxima. Para determinar a significância dos modelos de regressões lineares, foi utilizado o teste t de Student, para a verificação quais variáveis que são influenciadoras da oscilação do nível. Por fim, para indicar o grau de precisão dos modelos de regressões lineares avaliados, aplicou-se a metodologia do erro relativo médio quadrático. As direções de ventos que mostraram-se mais influenciadoras no processo de oscilação dos níveis d’água à jusante do canal São Gonçalo, foram as de sudeste (SE), sul (S) e oeste (O). A variável com maior influência no processo de oscilação dos níveis d’água à jusante da barragem eclusa do canal São Gonçalo, foi a velocidade máxima do vento (VMáx). / São Gonçalo is an important watercourse, belonging to the Mirim-São Gonçalo hydrographic basin, which has an area of 25,000 km² and is located in the coastal region of the state of Rio Grande do Sul. The Patos lagoon, which maintains a direct connection with the Atlantic Ocean, to Mirim lagoon, considered as a large reservoir of fresh water in southern Brazil. It’s situated in a lowland lowland region and presents great complexity and sensitivity to the fluctuations of water levels, both due to the regime and direction of the winds and the rainfall regime. The objective of this work was to evaluate the influence of wind and precipitation on the oscillation of the water level downstream of the São Gonçalo channel dam. The hypothesis was that the velocity and direction of the wind, along with the precipitation, influence the oscillation of the levels of the São Gonçalo channel. To achieve the objectives, multiple linear simple regression methods were used between water level, precipitation and wind velocities at 2 and 7 m in height and at maximum velocity. To determine the significance of the linear regression models, the Student's t test was used to verify which variables are influencing the level oscillation. Finally, to indicate the degree of precision of the linear regression models evaluated, the methodology of the relative mean square error was applied. The directions of the winds that were most influential in the process of oscillation of the water levels downstream of the São Gonçalo channel were those of the southeast (SE), south (S) and west (O). The variable with the greatest influence on the oscillation process of the water levels downstream of the São Gonçalo channel dam was the maximum wind speed.

O inventário florestal é uma das principais ferramentas na gestão dos recursos florestais, uma vez que as informações geradas por ele são utilizadas ao longo de toda a cadeia produtiva do setor. Desta forma, erros nas estimativas volumétricas dos inventários florestais devem ser controlados. Inúmeras informações podem ser obtidas a partir de imagens orbitais ou aerotransportadas, uma vez que podem cobrir facilmente toda a área de interesse, e estão comumente disponíveis em empresas florestais ou ao usuário final. A utilização de preditores derivados das imagens pode trazer benefícios para as estimativas do inventário florestal. Desta forma, a aplicação de técnicas de regressão linear múltipla (RLM) ganhou espaço no setor devido a sua facilidade de aplicação. Porém, a RLM não leva em consideração a dependência espacial entre as unidades amostrais, sendo que a geoestatística pode ser utilizada para predizer a distribuição espacial do estoque de madeira (VTCC) para uma dada região. A modelagem geoestatística mais simples como a krigagem ordinária (KO), por considerar apenas a dependência espacial entre os pontos não amostrados, pode apresentar erros de predição nestes locais. Tais erros podem ser reduzidos com a aplicação de técnicas mais robustas como a Krigagem com Deriva Externa (KDE), pois esta agrega as informações obtidas das imagens com a distribuição espacial do volume. Buscando-se avaliar as vantagens da integração do Sensoriamento Remoto (SR) ao inventário florestal foram testados 4 tipos diferentes de imagens; as oriundas dos satélites LANDSAT8, RAPIDEYE e GEOEYE, e as provenientes de aeronaves (Imagens Aerotransportadas). Avaliou-se também diferentes tipos de estimativas para a predição volumétrica sendo estas RLM, KDE e KO. A melhor estimativa serviu de variável auxiliar para o estimador de regressão (ER), sendo os resultados comparados com a abordagem tradicional da amostragem aleatória simples (AAS). Os resultados demonstraram por meio da validação cruzada que as estimativas da KDE foram mais eficientes que as estimativas da KO e da RLM. Os melhores preditores (variáveis auxiliares) foram aqueles derivados do satélite LANDSAT8 e do satélite RAPIDEYE. Obteve-se como produto das estimativas de KDE e RLM mapas capazes de detectar áreas com mortalidade ou anomalias em meio a formação florestal. A utilização de uma estimativa de KDE utilizando imagens LANDSAT8 como medida auxiliar para o ER permitiu reduzir o erro amostral da AAS de 3,87% para 2,34%. Da maneira tradicional, tal redução de erro apenas seria possível com um aumento de mais 99 unidades amostrais. / Forest Inventory (FI) is one of the main tools for managing forest resources, once the information derived from FI is used along the sector production chain. When estimating volume, errors resulting from FI are common, therefore these errors must be controlled. Once orbital or airborne imaging data are easily acquired for an entire area, and are commonly available in forest companies or for the end user, much information can be obtained from these products. The use of predictor derived from images can be of significant benefits to forest inventory estimates. For that reason, the application of linear multiple regression (LMR) techniques have taken place in the forest sector, due to the facilities of its application. However, the LMR technique does not take the spatial dependence among sample units in consideration, the geostatistics utilized to predict the spatial distribution of the wood stock (VTCC) for a specific region. Simpler geostatistical modeling as the ordinary kriging (OK), just takes in consideration the spatial dependence among non-sampled points, because of that, prediction errors can be found. Such errors can be reduced when techniques that are more robust are applied, such as the kriging with external drift (KED) approach. This technique aggregates the information obtained from the images with the spatial distribution of the volume. In order to evaluate the advantages of Remote Sensing and Forest Inventory integration, we considered 4 different types of images, from the satellites LANSAT 8, RAPIDEYE, GEOEYE and from airborne images. When predicting volume, three different approaches were evaluated: LMR, EDK, OK. The best model among those evaluated, served as auxiliary variable for the regression estimator (RE). The result were then compared to the traditional approach, simple random sampling (SRS).This approach showed, through a cross-validation, that the KDE estimates were more efficiently than the OK and the LMR. The best predictor model (auxiliary variables) were derived from LADNSAT 8 and RAPIDEYE satellites. There is a significant advantage to using the KDE and LMR approaches, as it allows for a spatial representation of areas with mortality or anomalies, in a forest environment. The combination of KDE approach and LANDSAT 8 images as an auxiliary method for the RE, abled the decrease of the sampling error of SRS from 3.87% to 2.34%.The traditional approaches to conduct plantation inventories would allow for this error reduction, only if there were an increase of 99 more sampling units.

Made available in DSpace on 2017-05-12T14:47:10Z (GMT). No. of bitstreams: 1 Ademir Natal Correa.pdf: 1505539 bytes, checksum: fd8e294f5766bf4043789d75eba28f1f (MD5) Previous issue date: 2007-07-16 / The objective of this study was to assess the relationship among soil physical attributes and their influences on wheat yield. For this purpose an estimating method, called State-Space Model or dynamic linear regression model, was used and compared to simple and multiple regression models of classical statistics. Experimental data were obtained at a Rhodic Ferralsol, originated from UNIOESTE Agricultural Engineering Experimental Nucleus Cascavel Campus, in an area where wheat was grown. In this area, 3 equally spaced transects, with 97 sampling points, 3.0 meters away from each other, were delimited. The State-Space approach was used to assess wheat yield estimate on position i, influenced by wheat yield, bulk density, soil compaction degree and soil resistance to penetration on position i-1 in different combination between data series of these variables. Applying the State-Space approach, all the response variables presented significant correlation with the dependent variable: soil resistance to penetration was the attribute with the best correlation, presenting R2 coefficient equal to 0.849. The other attributes had R2 coefficient of around 0.800. Comparing to conventional static models, soil resistance to penetration attribute had R2 coefficient equal to 0.102. The other attributes had R2 coefficient equal or less than 0.087, in conventional regression. Utilizing the State-Space approach, the two combinations that indicated the best results were: 1) between wheat yield and soil resistance to penetration that showed the best estimate to wheat yield with R2 coefficient equal to 0.849, while the same combination in conventional regression presented R2 equal to 0.102; 2) between wheat yield, soil compaction degree and soil resistance to penetration, with R2 coefficient equal to 0.836, while the same combination in classical regression presented R2 equal to 0.217. Thus, it is possible to show the advantage of the State-Space approach in relation to other more conventional regression methods for estimating and forecasting in soil-plant system relationship. / Este trabalho foi realizado com o objetivo de estudar o relacionamento entre os atributos físicos do solo e a influência destes na produtividade de trigo. Para isso, utilizou-se o método de estimação chamado de Modelo de Espaço de Estados ou modelo de regressão linear dinâmico, comparando-o aos modelos de regressão simples e múltipla da estatística clássica. Os dados experimentais foram obtidos em um Latossolo Vermelho-Escuro pertencente ao Núcleo Experimental de Engenharia Agrícola da Universidade Estadual do Oeste do Paraná Campus de Cascavel, em uma área cultivada com trigo. Foram demarcadas 3 transeções com 97 pontos de amostragem espaçados de 3 m entre si. A abordagem de Espaço de Estados foi usada para avaliar a estimativa da produtividade do trigo na posição i, influenciada por medidas da produtividade do trigo, da densidade do solo, do grau de compactação do solo e da resistência do solo à penetração na posição i-1, em diferentes combinações entre as séries de dados dessas variáveis. Com a aplicação da abordagem de Espaço de Estados, todas as variáveis explicativas utilizadas apresentaram correlação significativa com a variável dependente: a resistência do solo à penetração foi o atributo com a melhor correlação, apresentando o coeficiente de ajuste R2 igual a 0,849. Os demais atributos tiveram os coeficientes R2 em torno de 0,800. Comparando-se com os modelos estáticos convencionais, o atributo resistência do solo à penetração teve o coeficiente de ajuste R2 igual a 0,102 e os demais atributos tiveram os seus coeficientes R2 abaixo de 0,087, na regressão convencional. Utilizando a metodologia de Espaço de Estados, as duas combinações que indicaram os melhores resultados foram a combinação entre produtividade do trigo e resistência do solo à penetração, que apresentou a melhor estimativa para produtividade do trigo, com coeficiente R2 igual a 0,849. A mesma combinação na regressão convencional resultou em R2 igual a 0,102. A segunda melhor combinação ocorreu entre os atributos: produtividade do trigo, grau de compactação do solo e resistência do solo à penetração, com R2 igual a 0,836, sendo que a mesma combinação na regressão clássica teve o coeficiente R2 igual a 0,217. Com isso é possível mostrar-se a vantagem da abordagem de Espaço de Estados em relação a outros métodos de estimativa e previsão para o relacionamento no sistema solo-planta.

<p>Doktorska disertacija razmatra problem algoritamske dijagnostike<br />metaboličkog sindroma na osnovu lako merljivih parametara: pol,<br />starosna dob, indeks telesne mase, odnos obima struka i visine,<br />sistolni i dijastolni krvni pritisak. U istraživanju su primenjene i<br />eksperimentalno ispitane tri različite metode mašinskog učenja:<br />stabla odluke, linearna regresija i veštačke neuronske mreže.<br />Pokazano je da veštačke neuronske mreže daju visok nivo<br />prediktivnih vrednosti dovoljan za primenu u praksi. Korišćenjem<br />dobijenog rezultata definisan je i implementiran inteligentni<br />softverski sistem za dijagnostiku metaboličkog sindroma.</p> / <p>The doctoral dissertation examines the problem of algorithmic diagnostics of<br />the metabolic syndrome based on easily measurable parameters: sex, age,<br />body mass index, waist and height ratio, systolic and diastolic blood<br />pressure. In the study, three different methods of machine learning were<br />applied and experimentally examined: decision trees, linear regression and<br />artificial neural networks. It has been shown that artificial neural networks<br />give a high level of predictive value sufficient to be applied in practice. Using<br />the obtained result, an intelligent software system for the diagnosis of<br />metabolic syndrome has been defined and implemented.</p>

With the growing need to maintain roadway systems for provision of safety and comfort for travelers, network level decision-making becomes more vital than ever. In order to keep pace with this fast evolving trend, highway authorities must maintain extremely effective databases to keep track of their highway maintenance needs. Florida Department of Transportation (FDOT), as a leader in transportation innovations in the U.S., maintains Pavement Condition Survey (PCS) database of cracking, rutting, and ride information that are updated annually. Crack rating is an important parameter used by FDOT for making maintenance decisions and budget appropriation. By establishing a crack rating threshold below which traveler comfort is not assured, authorities can screen the pavement sections which are in need of Maintenance and Rehabilitation (M&R). Hence, accurate and reliable prediction of crack thresholds is essential to optimize the rehabilitation budget and manpower. Transition Probability Matrices (TPM) can be utilized to accurately predict the deterioration of crack ratings leading to the threshold. Such TPMs are usually developed by historical data or expert or experienced maintenance engineers' opinion. When historical data are used to develop TPMs, deterioration trends have been used vii indiscriminately, i.e. with no discrimination made between pavements that degrade at different rates. However, a more discriminatory method is used in this thesis to develop TPMs based on classifying pavements first into two groups. They are pavements with relatively high traffic and, pavements with a history of excessive degradation due to delayed rehabilitation. The new approach uses a multiple non-linear regression process to separately optimize TPMs for the two groups selected by prior screening of the database. The developed TPMs are shown to have minimal prediction errors with respect to crack ratings in the database that were not used in the TPM formation. It is concluded that the above two groups are statistically different from each other with respect to the rate of cracking. The observed significant differences in the deterioration trends would provide a valuable tool for the authorities in making critical network-level decisions. The same methodology can be applied in other transportation agencies based on the corresponding databases.

Job dissatisfaction among bank employees may adversely influence the financial performance of banks due to employee turnover, decreased productivity, poor service quality, decreased customer satisfaction, and negative employee attitudes in the workplace. The purpose of this correlational study was to examine how work on the present job, pay, opportunities for promotion, supervision, and coworker relationships predict job satisfaction among bank employees in Nigeria. The population of the study was 167 bank employees in 3 commercial banks in Nigeria. The 2-factor theory (TFT) served as the theoretical foundation in this study. Data collection was through a survey instrument called the job descriptive index. The results of the multiple linear regression analysis showed that the regression model significantly predicted job satisfaction, F (5, 95) = 10.806, p < .05, R2 = .363. Both supervision and coworker relationships were statistically significant predictors of job satisfaction among bank employees in Nigeria, while there were no statistically significant relationships between the predictors' work on the present job, pay, and opportunities for promotion, and the dependent variable, job satisfaction. The implications of this study for positive social change include the potential to provide senior bank executives with an understanding of factors that relate to job satisfaction among bank employees, including creating a desirable work environment, improving the quality of supervision in the organization, increasing job satisfaction, and making the organization more desirable for employees.

Bayesian statistics is an important approach to modern statistical analyses. It allows us to use our prior knowledge of the unknown parameters to construct a model for our data set. The foundation of Bayesian analysis is Bayes' Rule, which in its proportional form indicates that the posterior is proportional to the prior times the likelihood. We will demonstrate how we can apply Bayesian statistical techniques to fit a linear regression model and a hierarchical linear regression model to a data set. We will show how to apply different distributions to Bayesian analyses and how the use of a prior affects the model. We will also make a comparison between the Bayesian approach and the traditional frequentist approach to data analyses.

The dissertation deals with the accounting aspects of assessing the financial health of a company with a focus on agriculture. The main objective of this study is to assess individual methods designed to evaluate the financial health of a company, to determine their sensitivity to risk data in accounting. The study is focused on the field of agriculture mainly as a result of knowledge about the difficult process of compiling and using agricultural accounting. Agriculture fall within the primary sector of the economy, is very important for landscaping and a lot of subsidies flow from the budget of state and the European Union. Due to the specifics and stated problematic areas, which cannot be fully captured by legislation, incomplete or distorted information is transmitted, being also transferred to the methods of the financial health assessment of the company. Attention is also paid to the influence of legislative changes on the values in accounting as well as creative accounting. Following the findings from the theoretical basis, the application part analyses the impact of different accounting solutions on the financial statements. A paired t-test, used for the analysis, was preceded by data normality testing using the histogram and Shapiro-Wilk test. According to these tests, statistically significant differences were found com-paring the current method of accounting used for investment subsidies and leases with the IFRS accounting, between the accounting of changes in inventories and capitalization before and after 1 January 2016, and in land valuation using historical cost and market price. All these areas influence the values of all the analysed methods of financial health assessment. Only the CH-index showed no statistically significant difference in land valuation and accounting solution of inventory activation and changes. Furthermore, the reliability and controllability of the selected methods used for the evaluation of financial health in the field of agriculture is assessed. According to the results, none of the evaluated models can be used in its original variant, but it is possible to use them to compare the company with similar enterprises or over time thanks to the proven dependence of partial indicators and even the whole models on the productivity. Another type of analysis is designed to determine the indicators that have a statistically significant impact on the actual financial situation of businesses. The method of generalized linear models - multinomial linear regression - is used for this test. To determine whether an enterprise is at risk or not, it would be possible to use the stock / income and short-term liabilities / income indicators, and the cash flow / assets indicator to determine the type of threat.

Environmental sustainability in buildings is an important part of preserving the environment and reducing climate change. The increasing amount of physical infrastructure systems in Malawi has not been accompanied by policy-makers clearly understanding perceptions and attitudinal behaviors of building designers to promote environmental sustainability. Some building designers in Malawi might not be practicing sustainability innovations adequately, requiring more research to understand their perceptions and behaviors. The purpose of this mixed methods sequential and explanatory study was to explore how building designers' behaviors relate to the implementation of sustainability innovations in Malawi. Ajzen's theory of planned behavior explaining how attitudinal behaviors relate to individual's actions, served as the conceptual framework. The central research question investigated perceptions and attitudinal behaviors building designers hold about sustainability, and how these behaviors connect with practicing sustainability innovations. Data collection used a Likert scale questionnaire to capture behavior items. A sample of 99 individuals working in building organizations completed the questionnaire. Multiple linear regression analysis showed attitude behavior influenced practicing sustainability more than the subjective and perceived control behaviors. Interviews with 24 participants supported the analytical finding. Government and policy-makers were the target audience. Knowledge about behaviors toward sustainability innovations enables government and policy-makers strategize and change stakeholders' mindset to increase sustainability practices thereby impacting societal change in the construction communities.