50 resultados para Métodos estatísticos


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Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

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In this work, the quantitative analysis of glucose, triglycerides and cholesterol (total and HDL) in both rat and human blood plasma was performed without any kind of pretreatment of samples, by using near infrared spectroscopy (NIR) combined with multivariate methods. For this purpose, different techniques and algorithms used to pre-process data, to select variables and to build multivariate regression models were compared between each other, such as partial least squares regression (PLS), non linear regression by artificial neural networks, interval partial least squares regression (iPLS), genetic algorithm (GA), successive projections algorithm (SPA), amongst others. Related to the determinations of rat blood plasma samples, the variables selection algorithms showed satisfactory results both for the correlation coefficients (R²) and for the values of root mean square error of prediction (RMSEP) for the three analytes, especially for triglycerides and cholesterol-HDL. The RMSEP values for glucose, triglycerides and cholesterol-HDL obtained through the best PLS model were 6.08, 16.07 e 2.03 mg dL-1, respectively. In the other case, for the determinations in human blood plasma, the predictions obtained by the PLS models provided unsatisfactory results with non linear tendency and presence of bias. Then, the ANN regression was applied as an alternative to PLS, considering its ability of modeling data from non linear systems. The root mean square error of monitoring (RMSEM) for glucose, triglycerides and total cholesterol, for the best ANN models, were 13.20, 10.31 e 12.35 mg dL-1, respectively. Statistical tests (F and t) suggest that NIR spectroscopy combined with multivariate regression methods (PLS and ANN) are capable to quantify the analytes (glucose, triglycerides and cholesterol) even when they are present in highly complex biological fluids, such as blood plasma

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The time series analysis has played an increasingly important role in weather and climate studies. The success of these studies depends crucially on the knowledge of the quality of climate data such as, for instance, air temperature and rainfall data. For this reason, one of the main challenges for the researchers in this field is to obtain homogeneous series. A time series of climate data is considered homogeneous when the values of the observed data can change only due to climatic factors, i.e., without any interference from external non-climatic factors. Such non-climatic factors may produce undesirable effects in the time series, as unrealistic homogeneity breaks, trends and jumps. In the present work it was investigated climatic time series for the city of Natal, RN, namely air temperature and rainfall time series, for the period spanning from 1961 to 2012. The main purpose was to carry out an analysis in order to check the occurrence of homogeneity breaks or trends in the series under investigation. To this purpose, it was applied some basic statistical procedures, such as normality and independence tests. The occurrence of trends was investigated by linear regression analysis, as well as by the Spearman and Mann-Kendall tests. The homogeneity was investigated by the SNHT, as well as by the Easterling-Peterson and Mann-Whitney-Pettit tests. Analyzes with respect to normality showed divergence in their results. The von Neumann ratio test showed that in the case of the air temperature series the data are not independent and identically distributed (iid), whereas for the rainfall series the data are iid. According to the applied testings, both series display trends. The mean air temperature series displays an increasing trend, whereas the rainfall series shows an decreasing trend. Finally, the homogeneity tests revealed that all series under investigations present inhomogeneities, although they breaks depend on the applied test. In summary, the results showed that the chosen techniques may be applied in order to verify how well the studied time series are characterized. Therefore, these results should be used as a guide for further investigations about the statistical climatology of Natal or even of any other place.

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Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

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ARAÚJO, Marta Maria de. Formação do educador no curso de pedagogia de Caicó-RN: reprodução ou transformação social. Porto Alegre, 1985. Dissertação (Mestrado) - Curso de Pós-graduação em Educação. Universidade Federal do Rio Grande do Sul, Porto alegre, 1985

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Embora tenha sido proposto que a vasculatura retínica apresenta estrutura fractal, nenhuma padronização do método de segmentação ou do método de cálculo das dimensões fractais foi realizada. Este estudo objetivou determinar se a estimação das dimensões fractais da vasculatura retínica é dependente dos métodos de segmentação vascular e dos métodos de cálculo de dimensão. Métodos: Dez imagens retinográficas foram segmentadas para extrair suas árvores vasculares por quatro métodos computacionais (“multithreshold”, “scale-space”, “pixel classification” e “ridge based detection”). Suas dimensões fractais de “informação”, de “massa-raio” e “por contagem de caixas” foram então calculadas e comparadas com as dimensões das mesmas árvores vasculares, quando obtidas pela segmentação manual (padrão áureo). Resultados: As médias das dimensões fractais variaram através dos grupos de diferentes métodos de segmentação, de 1,39 a 1,47 para a dimensão por contagem de caixas, de 1,47 a 1,52 para a dimensão de informação e de 1,48 a 1,57 para a dimensão de massa-raio. A utilização de diferentes métodos computacionais de segmentação vascular, bem como de diferentes métodos de cálculo de dimensão, introduziu diferença estatisticamente significativa nos valores das dimensões fractais das árvores vasculares. Conclusão: A estimação das dimensões fractais da vasculatura retínica foi dependente tanto dos métodos de segmentação vascular, quanto dos métodos de cálculo de dimensão utilizados

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Trabalho com o objetivo de identificar as alterações do pé diabético causadas pelas lesões microangiopáticas e das lesões do fundo de olho secundárias aretinopatia diabética. Métodos:76 pacientes com Diabetes Melito tipos 1 e 2atendidos no ambulatório de Oftalmologia e Cirurgia Vascular do HUOL/UFRN, Natal, RN, no período de novembro de 2004 a janeiro de 2005, com queixas relativas a alterações da retinopatia diabéticae/oudo pé diabético. Em todos os pacientes foi realizado exame clínico geral, vascular e oftalmológico. Na avaliação específicado pé diabético deu-se ênfase paraa investigação do status vascular pela Classificação de Fontaine para Doença Arterial Obstrutiva Periférica, biomecânica,e teste do monofilamento de Semmes-Weinstein. O exame oftalmológico constou de refração e fundoscopiaatravés da qual identificou-se as formas clínicas da retinopatia diabética. Os dados foram submetidos à análise estatística das variáveis primárias que consistiu em caracterizar o grupo quanto a idade, tempo de doença, nível de glicose A segunda estratégia da análise dos dados constituiu na realização de testes de associação entrealgumas variáveis secundárias selecionadas. O software utilizado para os testes estatísticos foi o Statistica Versão 5, 1997.Resultado: Dos 76 pacientes diabéticos 97% tinham idade superior a 40 anos. O tempo de doença65% tinham mais de 10 anos. Com relação à glicose 72,72% apresentaram níveis de glicose em jejum acima de 100mg/dl. 55,26% apresentavam algum grau de retinopatia diabética contra 44,74% que não apresentavamesses sinais. Com as alterações do pé diabético, identificou-se 59,93% com lesões com área de predominância isquêmica, enquanto 41,07% tinham ausência de sinais. 58,82% apresentaram área de predominância neuropática, e 41,18% sem sinais de neuropatia. Dos com retinopatia diabética 78,57% tinham comprometimento isquêmico no pé e 47,62% tinham algum grau de neuropatia diabética. Observou-se que a retinopatia diabética não proliferativa, nos seus diversos graus de comprometimento apresentou-se com percentuais em torno de 80% junto às lesões do pé diabético, seja isquêmico ou neuropático. Dos pacientes que tinham retinopatia 60,46% tinham alterações biomecânicas dos pés. Conclusão: Concluiu-se que a RDNP leve foi mais freqüente nas lesões do pé diabético isquêmico, enquanto a RDNP severa mostrou-se mais presente no pé diabético neuropático

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The current study presents the characteristics of self-efficacy of students of Administration course, who work and do not work. The study was conducted through a field research, descriptive, addressed quantitatively using statistical procedures. Was studied a population composed of 394 students distributed in three Higher Education Institutions, in the metropolitan region of Belém, in the State of Pará. The sampling was not probabilistic by accessibility, with a sample of 254 subjects. The instrument for data collection was a questionnaire composed of a set of questions divided into three sections: the first related to sociodemographic data, the second section was built to identify the work situation of the respondent and the third section was built with issues related to General Perceived Self-Efficacy Scale proposed by Schwarzer and Jerusalem (1999). Sociodemographic data were processed using methods of descriptive statistics. This procedure allowed characterizing the subjects of the sample. To identify the work situation, the analysis of frequency and percentage was used, which allowed to classify in percentage, the respondents who worked and those that did not work, and the data related to the scale of self-efficacy were processed quantitatively by the method of multivariate statistics using the software of program Statistical Package for Social Sciences for Windows - SPSS, version 17 from the process of Exploratory Factor Analysis. This procedure allowed characterizing the students who worked and the students who did not worked. The results were discussed based on Social Cognitive Theory from the construct of self-efficacy of Albert Bandura (1977). The study results showed a young sample, composed the majority of single women with work experience, and indicated that the characteristics of self-efficacy of students who work and students who do not work are different. The self-efficacy beliefs of students who do not work are based on psychological expectations, whereas the students who work demonstrated that their efficacy beliefs are sustained by previous experiences. A student who does not work proved to be reliant in their abilities to achieve a successful performance in their activities, believing it to be easy to achieve your goals and to face difficult situations at work, simply by invest a necessary effort and trust in their abilities. One who has experience working proved to be reliant in their abilities to conduct courses of action, although know that it is not easy to achieve your goals, and in unexpected situations showed its ability to solve difficult problems

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Natural ventilation is an efficient bioclimatic strategy, one that provides thermal comfort, healthful and cooling to the edification. However, the disregard for quality environment, the uncertainties involved in the phenomenon and the popularization of artificial climate systems are held as an excuse for those who neglect the benefits of passive cooling. The unfamiliarity with the concept may be lessened if ventilation is observed in every step of the project, especially in the initial phase in which decisions bear a great impact in the construction process. The tools available in order to quantify the impact of projected decisions consist basically of the renovation rate calculations or computer simulations of fluids, commonly dubbed CFD, which stands for Computational Fluid Dynamics , both somewhat apart from the project s execution and unable to adapt for use in parametric studies. Thus, we chose to verify, through computer simulation, the representativeness of the results with a method of simplified air reconditioning rate calculation, as well as making it more compatible with the questions relevant to the first phases of the project s process. The case object consists of a model resulting from the recommendations of the Código de Obras de Natal/ RN, customized according to the NBR 15220. The study has shown the complexity in aggregating a CFD tool to the process and the need for a method capable of generating data at the compatible rate to the flow of ideas and are discarded during the project s development. At the end of our study, we discuss the necessary concessions for the realization of simulations, the applicability and the limitations of both the tools used and the method adopted, as well as the representativeness of the results obtained

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The assessment of building thermal performance is often carried out using HVAC energy consumption data, when available, or thermal comfort variables measurements, for free-running buildings. Both types of data can be determined by monitoring or computer simulation. The assessment based on thermal comfort variables is the most complex because it depends on the determination of the thermal comfort zone. For these reasons, this master thesis explores methods of building thermal performance assessment using variables of thermal comfort simulated by DesignBuilder software. The main objective is to contribute to the development of methods to support architectural decisions during the design process, and energy and sustainable rating systems. The research method consists on selecting thermal comfort methods, modeling them in electronic sheets with output charts developed to optimize the analyses, which are used to assess the simulation results of low cost house configurations. The house models consist in a base case, which are already built, and changes in thermal transmittance, absorptance, and shading. The simulation results are assessed using each thermal comfort method, to identify the sensitivity of them. The final results show the limitations of the methods, the importance of a method that considers thermal radiance and wind speed, and the contribution of the chart proposed

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Chitin is an important structural component of the cellular wall of fungi and exoskeleton of many invertebrate plagues, such as insects and nematodes. In digestory systems of insects it forms a named matrix of peritrophic membrane. One of the most studied interaction models protein-carbohydrate is the model that involves chitin-binding proteins. Among the involved characterized domains already in this interaction if they detach the hevein domain (HD), from of Hevea brasiliensis (Rubber tree), the R&R consensus domain (R&R), found in cuticular proteins of insects, and the motif called in this study as conglicinin motif (CD), found in the cristallography structure of the β-conglicinin bounded with GlcNac. These three chitin-binding domains had been used to determine which of them could be involved in silico in the interaction of Canavalia ensiformis and Vigna unguiculata vicilins with chitin, as well as associate these results with the WD50 of these vicilins for Callosobruchus maculatus larvae. The technique of comparative modeling was used for construction of the model 3D of the vicilin of V. unguiculata, that was not found in the data bases. Using the ClustalW program it was gotten localization of these domains in the vicilins primary structure. The domains R&R and CD had been found with bigger homology in the vicilins primary sequences and had been target of interaction studies. Through program GRAMM models of interaction ( dockings ) of the vicilins with GlcNac had been gotten. The results had shown that, through analysis in silico, HD is not part of the vicilins structures, proving the result gotten with the alignment of the primary sequences; the R&R domain, although not to have structural similarity in the vicilins, probably it has a participation in the activity of interaction of these with GlcNac; whereas the CD domain participates directly in the interaction of the vicilins with GlcNac. These results in silico show that the amino acid number, the types and the amount of binding made for the CD motif with GlcNac seem to be directly associates to the deleterious power that these vicilins show for C. maculatus larvae. This can give an initial step in the briefing of as the vicilins interact with alive chitin in and exert its toxic power for insects that possess peritrophic membrane

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Shrimp farming is one of the activities that contribute most to the growth of global aquaculture. However, this business has undergone significant economic losses due to the onset of viral diseases such as Infectious Myonecrosis (IMN). The IMN is already widespread throughout Northeastern Brazil and affects other countries such as Indonesia, Thailand and China. The main symptom of disease is myonecrosis, which consists of necrosis of striated muscles of the abdomen and cephalothorax of shrimp. The IMN is caused by infectious myonecrosis virus (IMNV), a non-enveloped virus which has protrusions along its capsid. The viral genome consists of a single molecule of double-stranded RNA and has two Open Reading Frames (ORFs). The ORF1 encodes the major capsid protein (MCP) and a potential RNA binding protein (RBP). ORF2 encodes a probable RNA-dependent RNA polymerase (RdRp) and classifies IMNV in Totiviridae family. Thus, the objective of this research was study the IMNV complete genome and encoded proteins in order to develop a system differentiate virus isolates based on polymorphisms presence. The phylogenetic relationship among some totivirus was investigated and showed a new group to IMNV within Totiviridae family. Two new genomes were sequenced, analyzed and compared to two other genomes already deposited in GenBank. The new genomes were more similar to each other than those already described. Conserved and variable regions of the genome were identified through similarity graphs and alignments using the four IMNV sequences. This analyze allowed mapping of polymorphic sites and revealed that the most variable region of the genome is in the first half of ORF1, which coincides with the regions that possibly encode the viral protrusion, while the most stable regions of the genome were found in conserved domains of proteins that interact with RNA. Moreover, secondary structures were predicted for all proteins using various softwares and protein structural models were calculated using threading and ab initio modeling approaches. From these analyses was possible to observe that the IMNV proteins have motifs and shapes similar to proteins of other totiviruses and new possible protein functions have been proposed. The genome and proteins study was essential for development of a PCR-based detection system able to discriminate the four IMNV isolates based on the presence of polymorphic sites

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

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The study of complex systems has become a prestigious area of science, although relatively young . Its importance was demonstrated by the diversity of applications that several studies have already provided to various fields such as biology , economics and Climatology . In physics , the approach of complex systems is creating paradigms that influence markedly the new methods , bringing to Statistical Physics problems macroscopic level no longer restricted to classical studies such as those of thermodynamics . The present work aims to make a comparison and verification of statistical data on clusters of profiles Sonic ( DT ) , Gamma Ray ( GR ) , induction ( ILD ) , neutron ( NPHI ) and density ( RHOB ) to be physical measured quantities during exploratory drilling of fundamental importance to locate , identify and characterize oil reservoirs . Software were used : Statistica , Matlab R2006a , Origin 6.1 and Fortran for comparison and verification of the data profiles of oil wells ceded the field Namorado School by ANP ( National Petroleum Agency ) . It was possible to demonstrate the importance of the DFA method and that it proved quite satisfactory in that work, coming to the conclusion that the data H ( Hurst exponent ) produce spatial data with greater congestion . Therefore , we find that it is possible to find spatial pattern using the Hurst coefficient . The profiles of 56 wells have confirmed the existence of spatial patterns of Hurst exponents , ie parameter B. The profile does not directly assessed catalogs verification of geological lithology , but reveals a non-random spatial distribution

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Conselho Nacional de Desenvolvimento Científico e Tecnológico