Applied statisticians are often faced with the problem of dealing with high dimensional data sets when attempting to describe the variability of a single set of variables, or trying to predict the variation of one set of variables from another. In this study, two data reduction methods are described: Redundancy Analysis and Partial Least Squares. A hybrid approach developed by Bougeard et al., (2007) and called Continuum Redundancy-Partial Least Squares, is described. All three methods are extended to the frequency domain in order to allow the lower dimensional subspace used to describe the variability to change with frequency. To illustrate and compare the three methods, and their frequency dependent generalizations, an idealized coupled atmosphere-ocean model is introduced in state space form. This model provides explicit expressions for the covariance and cross spectral matrices required by the various methods; this allows the strengths and weaknesses of the methods to be identified.

Sparse approximation is a Bayesian inference program with a wide number of signal processing applications, such as Compressed Sensing recovery used in medical imaging. Previous sparse coding implementations relied on digital algorithms whose power consumption and performance scale poorly with problem size, rendering them unsuitable for portable applications, and a bottleneck in high speed applications. A novel analog architecture, implementing the Locally Competitive Algorithm (LCA), was designed and programmed onto a Field Programmable Analog Arrays (FPAAs), using floating gate transistors to set the analog parameters. A network of 6 coefficients was demonstrated to converge to similar values as a digital sparse approximation algorithm, but with better power and performance scaling. A rate encoded spiking algorithm was then developed, which was shown to converge to similar values as the LCA. A second novel architecture was designed and programmed on an FPAA implementing the spiking version of the LCA with integrate and fire neurons. A network of 18 neurons converged on similar values as a digital sparse approximation algorithm, with even better performance and power efficiency than the non-spiking network. Novel algorithms were created to increase floating gate programming speed by more than two orders of magnitude, and reduce programming error from device mismatch. A new FPAA chip was designed and tested which allowed for rapid interfacing and additional improvements in accuracy. Finally, a neuromorphic chip was designed, containing 400 integrate and fire neurons, and capable of converging on a sparse approximation solution in 10 microseconds, over 1000 times faster than the best digital solution.

The term mobility is at the same time, cause and effect of urban development and is directly related to the economic, social and environmental aspects of a region. Your qualified plan implies harmonic, efficient and democratically run cities. Whereas parking lots are defined as infrastructure for urban mobility, the research objective was to identify the perceptions of users of municipal services paid parking lots in order to provide subsidies for the construction of the Mobility Plan Territorial Bagé. Therefore, the present study uses the model ECSI (European Customer Satisfaction Index) to measure relationships involving users of the municipal system of rotating Paid parking. ECSI was estimated by PLS-PM method that is characterized by its robustness in the face of structural models with missing data with normality. Data were collected through questionnaires and applied, non-randomly by 401 users Municipal Parking System Revolving in the city of Bagé, Rio Grande do Sul. The results indicate that the system user considers the level of service provided satisfactory (mean 7.65), and this construct most affected by the expectation of users and image of service. The construct with the lowest average was the perceived value (average 6.73). The constructs differ systematically only for income and age profiles, in other words, as age or income increase, the trend is that there is a better evaluation of the different constructs. / O termo mobilidade é, ao mesmo tempo, causa e efeito do desenvolvimento urbano e está diretamente relacionado com os aspectos econômicos, sociais e ambientais de uma região. O seu planejamento qualificado implica cidades harmônicas, eficientes e democraticamente geridas. Considerando que estacionamentos são definidos como infraestrutura de mobilidade urbana, o objetivo da pesquisa foi o de identificar a percepção dos usuários do serviço municipal de estacionamento rotativo a fim de fornecer subsídios para a construção do Plano de Mobilidade Territorial de Bagé. Para tanto, o presente estudo utiliza o modelo ECSI (Índice Europeu de Satisfação do Cliente) na mensuração das relações que envolvem os usuários do sistema municipal de estacionamento rotativo Pago. O ECSI foi estimado pelo método PLS-PM que se caracteriza por sua robustez diante de modelos estruturais compostos por dados com falta de normalidade. Os dados foram coletados mediante questionários aplicados e respondidos, de forma não aleatória, por 401 usuários do sistema municipal de estacionamento rotativo na cidade de Bagé, Rio Grande do Sul. Os resultados obtidos permitem concluir que o usuário do sistema considera o nível de serviço prestado satisfatório (média 7,65), sendo este constructo mais afetado pela expectativa dos usuários e imagem do serviço prestado. O constructo com menor média foi o valor percebido (média 6,73). Os constructos diferem de forma sistemática apenas para os perfis renda e idade, ou seja, à medida que a idade ou a renda aumentam, a tendência é de que exista uma melhor avaliação dos diferentes constructos.

O presente trabalho propõe um procedimento de valoração de marca a partir de uma modelagem por equações estruturais. Este estudo aponta inicialmente um modelo conceitual de valoração de marca baseado na linha comportamental, ou seja, nas percepções dos consumidores e não no valor monetário da marca. Para aplicação do procedimento proposto, foram escolhidas as marcas das operadoras de telefonia celular Vivo e Claro. A partir da modelagem por equações estruturais, ajustou-se o modelo teórico proposto a cada uma das marcas em questão. Com o modelo ajustado foi possível calcular não apenas o valor de marca para cada um dos consumidores, como todas as variáveis que o compõe. A possibilidade de se calcular o valor de marca para cada consumidor é extremamente inovadora, uma vez que nenhum dos modelos revistos na literatura científica aponta esta funcionalidade. Com a base de dados contendo o valor de marca para cada consumidor entrevistado, foi possível efetuar diversas análises estatísticas, que permitiram compreender de forma profunda quais variáveis mais impactam o valor das marcas, ou ainda quais são seus pontos fortes e fracos. Com isto, foi possível desenhar estratégias de marketing específicas para que cada uma das marcas aumente o seu valor. / The present study brings a procedure that measures brand equity using a structural equation modeling. This thesis proposes a conceptual model that measures brand equity based in a behaviorist approach, i.e., based on the consumers perception instead of monetary brand value. To apply this procedure two cellular phone operator brands were chosen: Vivo and Claro, and then, using structural equation modeling, it was possible to adjust the initial theory model to each brand. From the adjusted model it was possible to measure not only brand equity to each consumer, but all variables that are part of it. The possibility to measure brand equity to each consumer is extremely new, once no other models - reviewed in the scientific literature - pointed this feature. Using the database and the brand value to each interviewed consumer it was possible to make statistic analysis that allowed comprehending deeply which variables impact brand equity, or what are their strong and weak points. Then it was possible to make specific marketing strategies to each brand to increase their equity.

This thesis is concerned with data analysis, especially with principal component analysis and its sparse modi cation (SPCA), which is NP-hard-to- solve. SPCA problem can be recast into the regression framework in which spar- sity is usually induced with ℓ1-penalty. In the thesis, we propose to use iteratively reweighted ℓ2-penalty instead of the aforementioned ℓ1-approach. We compare the resulting algorithm with several well-known approaches to SPCA using both simulation study and interesting practical example in which we analyze voting re- cords of the Parliament of the Czech Republic. We show experimentally that the proposed algorithm outperforms the other considered algorithms. We also prove convergence of both the proposed algorithm and the original regression-based approach to PCA. vi

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This diploma thesis examines various linear regression methods and their use to establish regression relationships between the compressive strength of concrete determined by the indirect method and by the crushing of the specimens in the press. It deals mainly with the uncertainty of values measured by the indirect method, which is neglected by the usually used ordinary least squares regression method. It also deals with the weighted least squares method, suitable for so-called heteroskedastic data. It compares different regression methods on several sets of previously measured data. The final part of the work examines the effect of removing too influential points identified by Cook's distance, which may skew the regression results.

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Det finns olika aspekter som påverkar den ekonomiska tillväxten, där ibland inflation. Det har under många år pågått diskussioner om hur förhållandet mellan dessa två variabler ser ut. Denna uppsats analyserar sambandet mellan ekonomisk tillväxt och inflation. Utöver detta besvarar studien om inflation har någon statistisk signifikant relation till den ekonomiska tillväxten. I studien används paneldata från 19 OECD-medlemsländer från 1976 till 2020. För att analysera data utformas en regressionsmodell genom att använda BNP per capita som beroende variabel samt inkludera sju oberoende variabler (inflation, utländska direktinvesteringar, inhemska sparande, bytesvillkor, befolkningstillväxt, humankapital samt initial BNP). Resultatet från regressionsanalysen som utfördes genom fixed effects least squares dummy variable modellen tyder på ett negativt samband mellan inflation och BNP per capita. Däremot är resultatet enbart statistiskt signifikant om inte länder-, perioddummies eller initial BNP inkluderas. / There are various aspects that affect economic growth, including inflation. For many years, there have been discussions about the relationship between these two variables. This thesis analyzes the relation between economic growth and inflation. In addition, the study answers whether inflation has any statistically significant relation to economic growth. The study uses panel data from 19 OECD member countries from 1976 to 2020. To analyze the data, a regression model is designed by using ln GDP per capita as a dependent variable and including seven independent variables (inflation, foreign direct investment, domestic savings, terms of trade, population growth, human capital and initial GDP). The results of the regression analysis are conducted using the fixed effects least squares dummy variable model indicate a negative relationship between inflation and GDP per capita. However, the result is only shown to be statistically significant unless countries-, period dummies or initial GDP are included.