Thesis (Ph. D.)--University of Rhode Island, 2003. / Typescript. Includes bibliographical references (leaves 154-158).

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 172-185).

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 138-148).

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

Submitted by Filipe Correia (fcorreia@fgvmail.br) on 2011-10-21T15:48:52Z No. of bitstreams: 1 Dissertação_Filipe_Correia.pdf: 336583 bytes, checksum: cc01dcd1cdf01f077f687eb2a1f68fc8 (MD5) / Approved for entry into archive by Andrea Virginio Machado (andrea.machado@fgv.br) on 2011-10-31T11:25:19Z (GMT) No. of bitstreams: 1 Dissertação_Filipe_Correia.pdf: 336583 bytes, checksum: cc01dcd1cdf01f077f687eb2a1f68fc8 (MD5) / Made available in DSpace on 2011-10-31T11:26:35Z (GMT). No. of bitstreams: 1 Dissertação_Filipe_Correia.pdf: 336583 bytes, checksum: cc01dcd1cdf01f077f687eb2a1f68fc8 (MD5) Previous issue date: 2011-07-07 / This paper aims to study the fiscal policy effects on a wide range of US macroeconomic variables. The empirical work is based upon a structural VAR with latent factors (FAVAR) and for which we develop a special identification scheme. As we estimate the factors using a principal component approach, theses estimates are very similar to observed industrial production and interest rate time series, and this is crucial for identification and instruments choice in our VAR model. Using impulse response functions we can see both production and personal consumption increase after a government expenditure exogenous shock. This effect of government expenditure is also positive among different income groups and further we find out lower classes are affected at the most after a public expenditure shock. This means this kind of consumers are probably subject to some credit rationing which difficults them to smooth consumption after an aggregate shock. / O objetivo desse artigo é analisar o impacto da política fiscal sobre diversas variáveis macroeconômicas dos EUA. A metodologia do trabalho empírico baseia-se em um modelo VAR estrutural que incorpora fatores latentes (FAVAR) e para o qual desenvolve-se um esquema de identificação específico. Visto que os fatores são estimados por principal components, estes aproximam-se muito das séries observadas de produção industrial e taxa de juros. Como será visto, este resultado é de fundamental importância para a hipótese de identificação e a escolha dos instrumentos do modelo VAR. Por meio das funções de resposta ao impulso analisa-se os efeitos de um aumento do gasto do governo sobre variáveis de produto e consumo e, por sua vez, corroborando a hipótese de que tanto o PIB quanto as despesas de consumo das famílias aumentam depois desse choque exógeno. Em particular esse efeito sobre o consumo também é verificado quando separamos os indivíduos em várias classes de acordo com renda. Olhando cuidadosamente no entanto pode-se perceber que um aumento no gasto público possui mais impacto sobre os consumidores de renda mais baixa. Ou seja, é provável que por estarem sujeitas a restrições de crédito, as classes mais baixas tem mais dificuldade em suavizar o consumo após um choque agregado.

With an increasing number of students enrolling at higher education institutions in South Africa, it has become important to investigate whether these institutions are using their resources adequately. This study uses data envelopment analysis (DEA) to estimate the efficiency of 23 South African tertiary education institutions based on both teaching and research outputs. Using DEA we are able to rank South African universities according to their use of resources in these two areas. These rankings can identify institutions which are performing well and also those which require improvement. The effect that merging institutions has on this efficiency is also determined. Owing to the limited sample size, variable reduction techniques, including the efficiency contribution measure (ECM) and principal components analysis (PCA-DEA), were used to improve the discrimination of the analysis.

Principal surfaces are smooth two-dimensional surfaces that pass through the middle of a p-dimensional data set. They minimise the distance from the data points, and provide a nonlinear summary of the data. The surfaces are nonparametric and their shape is suggested by the data. The formation of a surface is found using an iterative procedure which starts with a linear summary, typically with a principal component plane. Each successive iteration is a local average of the p-dimensional points, where an average is based on a projection of a point onto the nonlinear surface of the previous iteration. Biplots are considered as extensions of the ordinary scatterplot by providing for more than three variables. When the difference between data points are measured using a Euclidean embeddable dissimilarity function, observations and the associated variables can be displayed on a nonlinear biplot. A nonlinear biplot is predictive if information on variables is added in such a way that it allows the values of the variables to be estimated for points in the biplot. Prediction trajectories, which tend to be nonlinear are created on the biplot to allow information about variables to be estimated. The goal is to extend the idea of nonlinear biplot methodology onto principal surfaces. The ultimate emphasis is on high dimensional data where the nonlinear biplot based on a principal surface allows for visualisation of samples, variable trajectories and predictive sets of contour lines. The proposed biplot provides more accurate predictions, with an additional feature of visualising the extent of nonlinearity that exists in the data.

No description available.

This dissertation extends the work done on single component maintenance planning to a multi-component series system. An attempt is made to develop a function which represents the expected cost rate (cost per unit time) of any maintenance plan. Three increasingly complex cases are considered. The first and simplest case assumes that the component is restored to an “as good as new” condition after a maintenance operation. The second case assumes that an occasional imperfect maintenance Operation may occur. During this period of time, the failure rate of the component is higher. Hence, the likelihood of a failure is greater until the component is properly maintained in a subsequent maintenance operation. The final case assumes that there is some deterioration in the component behavior even after a maintenance operation. Therefore, it is necessary to replace the system at some point in time. Models for all three cases are developed. Based on these models, cost rate functions are constructed. The cost rate functions reflect the cost rates of maintaining a component at a particular time. In addition, the savings obtained through the simultaneous maintenance of components is also accounted for in the cost rate functions. A series of approximations are made in order to make the cost rate functions mathematically tractable. Finally, an algorithmic procedure for optimizing the cost rate functions for all three cases is given. / Ph. D.

Approved for public release; distribution in unlimited. / PC and MNF transforms are two widely used methods that are utilized for various applications such as dimensionality reduction, data compression and noise reduction. In this thesis, an in-depth study of these two methods is conducted in order to estimate their performance in hyperspectral imagery. First the PCA and MNF methods are examined for their effectiveness in image enhancement. Also, the various methods are studied to evaluate their ability to determine the intrinsic dimension of the data. Results indicate that, in most cases, the scree test gives the best measure of the number of retained components, as compared to the cumulative variance, the Kaiser, and the CSD methods. Then, the applicability of PCA and MNF for image restoration are considered using two types of noise, Gaussian and periodic. Hyperspectral images are corrupted by noise using a combination of ENVI and MATLAB software, while the performance metrics used for evaluation of the retrieval algorithms are visual interpretation, rms correlation coefficient spectral comparison, and classification. In Gaussian noise, the retrieved images using inverse transforms indicate that the basic PC and MNF transform perform comparably. In periodic noise, the MNF transform shows less sensitivity to variations in the number of lines and the gain factor. / Lieutenant, Hellenic Navy