The transfer pricing, literature indicates that an investigation of some aspects of this subject could usefully be undertaken in order to contribute to the understanding of transfer pricing in both domestic and international markets. This study aims at exploring the current state of transfer pricing practice and establishing the importance attached to the ranking of transfer pricing determinants (i. e. objectives and environmental variables) and the extent to which the ranking varies across markets, industry, and according to the transfer pricing method used. It also seeks to discover interrelationship among the transfer pricing determinants in order to produce a reduced set of basic factors. Lastly, it aims at evaluating the relationship between transfer pricing determinants and transfer pricing methods and at discovering a means of predicting the latter from the company's perception of the relative importance of these determinants. To achieve the above objectives, an empirical study covering both domestic and international markets was undertaken in UK companies. The conclusions are concerned with transfer pricing policy, methods currently used, and problems apparent in practice. The overall ranking-by survey respondents of the transfer pricing determinants is given as well as the results of tests of certain hypotheses which relate to this ranking. The transfer pricing determinants used in the survey for domestic and international. markets (twelve and twenty respectively) have been reduced by Factor Analysis to four and six factors. The study made use of the results to obtain measures of the ranking of discovered factors. Finally, the relationship between the transfer pricing determinants and transfer pricing methods was quantitatively evaluated in the form of a set of classification functions by using Multi-Discriminant Analysis. The classification functions are able to predict the transfer pricing method actually used in companies with an acceptable degree of success. The study's results have been reviewed with a small number of senior managers who are involved in establishing transfer pricing policy within their companies.

The difficulty of gathering information on field and coarse resolution of Landsat images forced to use ancillary data in vegetation mapping. The aim of this study is to increase the accuracy of species level vegetation classification incorporating environmental variables in the Amanos region. In the first part of the study, coarse vegetation classification is attained by using maximum likelihood method with the help of forest management maps. Canonical Correspondence analysis is used to explore the relationships among the environmental variables and vegetation classes. Discriminant Analysis is used in the second part of the study in two different stages. Firstly Fisher&rsquo / s linear equations for each of the previously defined nine groups calculated and the pixels are included in one of these groups by looking at the probability of that pixel being in that group. In the second stage Distance raster value of maximum likelihood classification is used. Distance raster pixels having a value less than one is accepted as misclassified and replaced with a value of first stage result of that pixel. As a result of this study 19.6 % increase in the overall accuracy is obtained by using the relationships between environmental variables and vegetation distribution.

Thesis (Ph. D.)--University of Texas at El Paso, 2008. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 36-44). Also available on the Internet.

Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Computer Science, 2009. / Includes bibliographical references.

Σε αυτή την εργασία ασχοληθήκαμε με δύο μεθόδους, που σκοπός τους είναι να κατατάσσουν τις παρατηρήσεις σε γνωστές ομάδες και στη συνέχεια να κάνουν προβλέψεις για καινούριες παρατηρήσεις. Αυτές οι μέθοδοι είναι η λογιστική παλινδρόμηση (logistic regression) και η διαχωριστική ανάλυση (discriminant analysis). Στο πρώτο κεφάλαιο αναφέραμε περιληπτικά τα μη γραμμικά μοντέλα παλινδρόμησης (αφού και η λογιστική παλινδρόμηση είναι ένα τέτοιο μοντέλο). Απλά αναφέρουμε τη μορφή που έχουν αυτά τα μοντέλα, με ποιες μεθόδους μπορούμε να εκτιμήσουμε τις παραμέτρους παλινδρόμησης, ποια είναι τα διαστήματα εμπιστοσύνης για τους συντελεστές παλινδρόμησης και τη μορφή που θα έχουν οι έλεγχοι υποθέσεων. Στο δεύτερο κεφάλαιο περιγράφουμε τη λογιστική παλινδρόμηση. Η λογιστική παλινδρόμηση είναι χρήσιμη σε καταστάσεις στις οποίες επιθυμούμε να προβλέψουμε την ύπαρξη ή την απουσία ενός χαρακτηριστικού ή ενός συμβάντος. Η πρόβλεψη αυτή βασίζεται στην κατασκευή ενός μοντέλου και συγκεκριμένα στον προσδιορισμό των τιμών που παίρνουν οι συντελεστές. Αυτή η μέθοδος είναι μια γενίκευση της απλή γραμμικής παλινδρόμησης για την περίπτωση όπου η εξαρτημένη μεταβλητή είναι δίτιμη (παίρνει την τιμή 0 όταν το χαρακτηριστικό απουσιάζει και την τιμή 1 όταν υπάρχει το χαρακτηριστικό). Στο τρίτο κεφάλαιο αναλύουμε τη διαχωριστική ανάλυση, η οποία έχει δύο στόχους: να χωρίσει ένα πληθυσμό σε ευδιάκριτες ομάδες και με τη βοήθεια ενός διαχωριστικού κανόνα να κατατάσσει παρατηρήσεις στις ευδιάκριτες ομάδες. Στο τέλος του κεφαλαίου περιγράφουμε τις ομοιότητες και τις διαφορές της διαχωριστικής ανάλυσης και της λογιστικής παλινδρόμησης. Στο τέταρτο και τελευταίο κεφάλαιο απλά δίνουμε ένα παράδειγμα που το λύνουμε με τη μέθοδο της λογιστικής παλινδρόμησης και ένα παράδειγμα που το λύνουμε με τη μέθοδο της διαχωριστικής ανάλυσης. Αυτό το κάνουμε με τη βοήθεια του στατιστικού πακέτου SPSS. / In this work we dealt with two methods, that their aim are to classify the observations in known teams and afterwards to make forecasts for new observations. These methods are the accountant regression (logistic regression) and the bisector analysis (discriminant analysis).

Made available in DSpace on 2015-03-26T13:32:07Z (GMT). No. of bitstreams: 1 texto completo.pdf: 1250297 bytes, checksum: 6fd0be6a8317d32468f8f7b7ff74d7ef (MD5) Previous issue date: 2009-02-17 / Fundação de Amparo a Pesquisa do Estado de Minas Gerais / The concern in minimizing the amount of chemical products used in farmings is increasing. The use of artificial vision systems has been demonstrating a great potential for use of varied taxes of inputs, as for instance, the application herbicides only in places where the presence of harmful plant is detected. The good acting of a system developed for this purpose depends mainly of the characteristic use that they allow to differentiate patterns of harmful plants of the pattern of the cultivated species. Like this, the objective of the present work was to develop and to evaluate a characteristic for the recognition of the patterns corn plant and harmful plant. The specific objectives were: the) to identify which image, green excess or the index of vegetation of normalized green, tends to provide better classification; b) to compare the classification obtained by characteristics geoestatistics, obtained when using the characteristics of Haralick. With this purpose, were acquired to the 29 days after the emergency, period in that it is usually made the application of herbicides, nine corn images (Zea Mays L.) and of each one of the species of appraised harmful plants in this experiment: Euphorbia heterophylla L., Digitaria horizontalis Willd, Cenchrus chinatus L. Six of these images were used for the selection of the characteristic that promotes better acting in the classification. The remaining three were used for the validation of the selected characteristic. Each one of the six training images was cut out in 100 blocks of 68x68 pixels. For each one of the blocks was obtained the value of the characteristic textural geoestatistic (variogram, the madogram, cross variogram and pseudo cross variogram) and the one of Haralick (angular moment, average, variance, entropy, correlation, moment of the product, inverse moment of the difference and correlation measures). Additionally, characteristic geoestatísticos and no-geoestatísticos they were obtained considering different angles (0, 45, 90 and 135°) of relationship among pixels. Characteristic geoestatistics were, also, obtained for different distances (1, 2, 3, 4, 5, 6, 7, 8, 9, 10) of in pairs among pixels. The characteristic variograma and madograma were calculated to leave of the image green excess and GNDVI. Already the characteristic cross variogram and pseudo cross variogram were calculated with Greenness Method use in the blocks using the combinations of the bands RxG, GxB and IVxG. The characteristic of Haralick were calculated starting from the images of the green excess and GNDVI. The acting of the characteristic, proposed like this, it was evaluated using discriminate analysis. The selected characteristic were those that presented larger value for the index kappa. Additionally, new characteristic were obtained starting from combinations of the selected characteristic. These combinations, also, had appraised acting using the discriminant analysis with the objective of to identify which combination provides better classification. Later, the power of generalization of the selected combination was evaluated using the three images of each species reserved for the validation stage. The conclusions obtained regarding the objectives proposed in this research were a) the image that tended to present the best results of the index kappa was the image excess of green; b) the characteristic obtained starting from the function madograma and the one of Haralick were the ones that supplied the best results; c) the characteristic geoestatistic madograma in the 10 distances and angle 0° presented better classification results when used without combination of other characteristic; d) the characteristic geoestatísticos and the one of Haralick, when used separately didn't present such good as combined results; e) the characteristic use that consider the continuity of the pixel values, in the recognition of patterns can be a fundamental tool in the classification process. / A preocupação em minimizar a quantidade de produtos químicos utilizado em lavouras vem aumentando. O uso de sistemas de visão artificial tem demonstrado um grande potencial para o uso de taxas variadas de insumos, como por exemplo, a aplicação de herbicidas somente em locais onde é detectada a presença de planta daninha. O bom desempenho de um sistema desenvolvido para esta finalidade depende principalmente do uso de descritores que permitam diferenciar padrões de plantas daninhas do padrão da espécie cultivada. Sendo assim, objetivo geral do presente trabalho foi desenvolver e avaliar um descritor para o reconhecimento dos padrões planta de milho e planta daninha. Os objetivos específicos foram: a) identificar qual imagem, excesso de verde ou o índice de vegetação de verde normalizado, tende a proporcionar melhor classificação; b) comparar a classificação obtida por descritores geoestatísticos, com a obtida ao usar os descritores de Haralick. Com esta finalidade, foram adquiridas aos 29 dias após a emergência, período em que normalmente é feita a aplicação de herbicidas, nove imagens de milho (Zea Mays L.) e de três espécies de plantas daninhas avaliadas neste experimento: leiteira (Euphorbia heterophylla L.), capim-milhã (Digitaria horizontalis Willd) timbête (Cenchrus echinatus L.). Seis destas imagens foram utilizadas para a seleção do descritor que promove melhor desempenho na classificação. As três restantes foram utilizadas para a validação do descritor selecionado. Cada uma das seis imagens de treinamento foi recortada em 100 blocos de 68x68 pixels. Para cada um dos blocos foi obtido o valor dos descritores texturais geoestatísticos (variograma, o madograma, variograma cruzado e pseudo variograma cruzado) e os de Haralick (momento angular, média, variância, entropia, correlação, momento do produto, momento inverso da diferença e medidas de correlação). Adicionalmente, descritores geoestatísticos e não-geoestatísticos foram obtidos considerando diferentes ângulos (0, 45, 90 e 135°) de relacionamento entre pixels. Descritores geoestatísticos foram, também, obtidos para diferentes distâncias (1, 2, 3, 4, 5, 6, 7, 8, 9, 10) de pareamento entre pixels. Os descritores variograma e madograma foram calculados partir da imagem excesso de verde e GNDVI. Já os descritores variograma cruzado e pseudo variograma cruzado foram calculados com o uso do Greenness Method nos blocos usando as combinações das bandas RxG, GxB e IVxG. Os descritores de Haralick foram calculados a partir das imagens do excesso de verde e GNDVI. O desempenho dos descritores, assim propostos, foi avaliado usando análise discriminante. Os descritores selecionados foram aqueles que apresentaram maior valor para o índice kappa. Adicionalmente, novos descritores foram obtidos a partir de combinações dos descritores selecionados. Estas combinações, também, tiveram o seu desempenho avaliado usando a análise discriminante com o objetivo de identificar qual combinação proporciona melhor desempenho na classificação. Posteriormente, o poder de generalização da combinação selecionada foi avaliado usando as três imagens de cada espécie reservadas para a etapa de validação. As conclusões obtidas com relação aos objetivos propostos nesta pesquisa foram a) a imagem que tendeu a apresentar os melhores resultados do índice kappa foi a imagem excesso de verde; b) os descritores obtidos a partir da função madograma e os de Haralick foram os que forneceram os melhores resultados; c) o descritor geoestatístico madograma nas 10 distâncias e ângulo 0° apresentou melhores resultados de classificação quando usado sem combinação de outros descritores; d) os descritores geoestatísticos e os de Haralick, quando usados isoladamente não apresentaram resultados tão bons quanto combinados; e) o uso de descritores que consideram a continuidade dos valores de pixel, no reconhecimento de padrões pode ser uma ferramenta fundamental no processo de classificação.

Made available in DSpace on 2015-03-26T13:32:08Z (GMT). No. of bitstreams: 1 texto completo.pdf: 958045 bytes, checksum: a7576f2d825f5b6d63c2110fc9afce32 (MD5) Previous issue date: 2009-12-15 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The correct classification of individuals in pre-established groups has become of great importance in breeding. The multivariate statistical techniques commonly used in this type of problem are the discriminant functions of Fisher and Anderson, which are used to allocate an initially unknown individual in one of the g populations or pre-defined groups. In recent decades a new computing paradigm, artificial neural networks, has come along to solve various problems of Statistics, such as grouping of similar individuals, time series forecasting and also of particular interest, the problem of classification. The objective of this research was to conduct a simulation study in order to compare the discriminant functions of Fisher and Anderson and neural networks. We evaluated the number of incorrect classifications of individuals known to belong to different populations with different levels of dissimilarity measured by the Mahalanobis distance. Simulations were conducted using the software Genes (Cruz, 2006). Although Artificial Neural Networks presented a rate of incorrect classification of individuals rejected for being considered ambiguous as to its discriminatory characteristics, it proved to be a promising technique, since it presented a lower number of incorrect classifications of individuals when compared to the discriminant functions. / A correta classificação de indivíduos em grupos pré-estabelecidos tem se tornado de grande importância no melhoramento genético. As técnicas de estatística multivariada usualmente utilizadas nesse tipo de problema são as funções discriminantes de Fisher e as funções discriminantes de Anderson, que são usadas para alocar um indivíduo inicialmente desconhecido em uma das g populações ou grupos pré-definidos. Nas últimas décadas vêm surgindo um novo paradigma de computação, as redes neurais artificiais, que podem ser utilizadas para resolver diversos problemas da Estatística, como agrupamento de indivíduos similares, previsão de séries temporais e em especial, os problemas de classificação. O objetivo dessa pesquisa foi realizar um estudo comparativo entre as funções discriminantes de Fisher e de Anderson e as redes neurais artificiais quanto ao número de classificações erradas de indivíduos sabidamente pertencentes a diferentes populações, com distintos níveis de dissimilaridade. Essa dissimilaridade, medida pela distância de Mahalanobis, foi um conceito de fundamental importância na utilização das técnicas de discriminação, pois quantificou o quanto as populações eram divergentes. Quanto maior o valor observado para essa medida, menos similares foram as populações em análise. A obtenção dos dados foi feita através de simulação utilizando o programa computacional Genes (CRUZ, 2006). As redes neurais artificiais apresentaram uma taxa de indivíduos rejeitados por serem considerados ambíguos quanto às suas características discriminatórias. No entanto, mostraram-se uma técnica promissora no que diz respeito a problemas de classificação, uma vez que apresentaram um número de classificações erradas de indivíduos menor que aqueles dados pelas funções discriminantes.

This mater thesis is focused on building predictive models. Their fundamental task is to provide an early-warning system, giving information about potential enterprise bankruptcy. The main essence and aim of the thesis is to create multivariate classification models by using discriminant analysis and logistic regression. Emphasis is put on their predictive accuracy, which is assessed for period of three years before bankruptcy declaration. Attempts to optimize classification thresholds in order to increase the initial accuracy are also made. Evaluating classification reliability of several existing models and performing profile analysis assessing predictive ability of univariate ratios were accomplished as well.

Novas regiões vitícolas estão se estabelecendo em muitas regiões brasileiras. Em especial, no estado do Rio Grande do Sul diversos empreendimentos estão instalados ou em fase de projeto de instalação em várias regiões. No contexto da moderna vitivinicultura nacional, um fator importante de agregação de valor aos produtos resultantes da atividade é a sua associação a uma identidade regional, tendência próxima ao conceito de terroir. Tratando-se de regiões produtoras novas, faz-se necessário um complexo levantamento de características de cada região produtora, de modo que sejam definidos parâmetros de diferenciação que confiram, a cada região, a necessária tipicidade tão próxima à ideia de terroir. Este trabalho propõe-se a trazer uma contribuição à caracterização de algumas novas regiões vitícolas do Rio Grande do Sul, tentando mostrar que é possível evidenciar fatores físicos que diferenciam cada região. Foram escolhidas três áreas na metade sul do estado, sendo duas na Campanha Gaúcha (Almadén em Santana do Livramento e Seival em Candiota), e uma na Serra do Sudeste (Chandon em Encluzilhada do Sul); também foi estudada uma área na Serra Gaúcha (Boscato em Nova Pádua). Como ferramentas de estudo, foram utilizadas técnicas de espectrorradiometria para levantamento de dados de campo nas quatro regiões, onde foram tomados espectros de reflectância foliar no visível e no infravermelho próximo e médio. Foram selecionadas parcelas de parreirais das variedades Cabernet Sauvignon, Merlot, Pinot Noir, Chardonnay e Riesling Itálico. Também foram utilizadas imagens de satélite (ASTER) para estudar as áreas da Almadén e Seival, com dados de reflectância no visível e no infravermelho de diversos cultivares de Vitis vinifera. Os dados foram analisados usando-se diversas técnicas de separação, como algoritmos de classificação supervisionada e Análise Discriminante. Os resultados, tanto para os dados de radiometria de campo quanto para os dados orbitais, mostraram que a partir da reflectância de folhas e de dossel é possível separar cada uma das regiões, com acurácias da ordem de 80% ou mais, sendo esta separabilidade atribuída à influência do meio físico sobre as plantas. Conclui-se que a utilização de dados e técnicas de Sensoriamento Remoto, com o apoio de técnicas de análise estatística, constitui relevante ferramenta de apoio à caracterização de regiões vitícolas no Rio Grande do Sul, e provavelmente para qualquer região produtora. / New viticultural regions are being created in several regions across Brazil. For instance, in Rio Grande do Sul State many wineries are already established, are being installed or are in project phase. In the context of modern Brazilian viticulture, an important factor for added value to products from the activity is its association to a regional identity, a tendency which is akin to the terroir concept. Being new regions, a comprehensive survey of the characteristics of each producing area is necessary, a step leading to the definition of parameters of differentiation, which will give to each region the required typicity, crucial to the terroir idea. Presently, we carry out a contribution to the characterization of some new viticultural regions of Rio Grande do Sul, trying to show that it is possible to bring to light physical factors which will differentiate each region. Three areas in the State’s Metade Sul (southern half) were selected, being two in the Campanha Gaúcha region (Almadén in Santana do Livramento and Seival in Candiota), and another one at the Serra do Sudeste (Chandon in Encruzilhada do Sul); we also selected a winery at the Serra Gaúcha (Boscato in Nova Pádua). As tools for this study, we used techniques of spectroradiometry to collect field data in all four regions, acquiring spectra of leaf reflectance in visible, wavelengths, and at near and mean infrared as well. We selected vineyards of the grape varieties Cabernet Sauvignon, Merlot, Pinot Noir, Chardonnay and Riesling Itálico. Satellite images (ASTER product) were also taken to study the Almadén and Seival wineries, using reflectance data in visible and infrared for some varieties of Vitis vinifera. All data were analyzed through several techniques intended for differentiation, as algorithms for supervised classification and, in Statistics, Discriminant Analysis. The results, from radiometry field data and from satellite data as well, showed that from the reflectance of leaves and canopy it is possible to separate each region, with accuracies as high as 80% and even more. This separability is believed to be due to the influence of the physical environment on plants. It is concluded that the use of data and techniques from Remote Sensing, associated with techniques of statistical analysis, are relevant tools to support the characterization of viticultural regions in Rio Grande do Sul and probably in any producing region.