993 resultados para ANOVA


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Dissertação mest., Gestão e Conservação da Natureza, Universidade do Algarve, 2009

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Metal matrix composites (MMC) having aluminium (Al) in the matrix phase and silicon carbide particles (SiCp) in reinforcement phase, ie Al‐SiCp type MMC, have gained popularity in the re‐cent past. In this competitive age, manufacturing industries strive to produce superior quality products at reasonable price. This is possible by achieving higher productivity while performing machining at optimum combinations of process variables. The low weight and high strength MMC are found suitable for variety of components

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Resumen tomado de la publicaci??n

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Given a reproducing kernel Hilbert space (H,〈.,.〉)(H,〈.,.〉) of real-valued functions and a suitable measure μμ over the source space D⊂RD⊂R, we decompose HH as the sum of a subspace of centered functions for μμ and its orthogonal in HH. This decomposition leads to a special case of ANOVA kernels, for which the functional ANOVA representation of the best predictor can be elegantly derived, either in an interpolation or regularization framework. The proposed kernels appear to be particularly convenient for analyzing the effect of each (group of) variable(s) and computing sensitivity indices without recursivity.

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Ejercicio resuelto paso a paso sobre el contraste ANOVA de un factor y contrastes a posteriori.

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Tema: Diseños con Factores Ortogonales

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The aim of this study was to analyze the evolution of socioeconomic inequalities in mortality due to ischemic heart diseases (IHD) in the census tracts of nine Spanish cities between the periods 1996–2001 and 2002–2007. Among women, there are socioeconomic inequalities in IHD mortality in the first period which tended to remain stable or even increase in the second period in most of the cities. Among men, in general, no socioeconomic inequalities have been detected for this cause in either of the periods. These results highlight the importance of intra-urban inequalities in mortality due to IHD and their evolution over time.

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Análisis multivariante con técnicas de Permutaciones y MANOVA (Permanova)

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Introducción y conceptos de ANOVA

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The FANOVA (or “Sobol’-Hoeffding”) decomposition of multivariate functions has been used for high-dimensional model representation and global sensitivity analysis. When the objective function f has no simple analytic form and is costly to evaluate, computing FANOVA terms may be unaffordable due to numerical integration costs. Several approximate approaches relying on Gaussian random field (GRF) models have been proposed to alleviate these costs, where f is substituted by a (kriging) predictor or by conditional simulations. Here we focus on FANOVA decompositions of GRF sample paths, and we notably introduce an associated kernel decomposition into 4 d 4d terms called KANOVA. An interpretation in terms of tensor product projections is obtained, and it is shown that projected kernels control both the sparsity of GRF sample paths and the dependence structure between FANOVA effects. Applications on simulated data show the relevance of the approach for designing new classes of covariance kernels dedicated to high-dimensional kriging.

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Mode of access: Internet.