5,889 research outputs found

    Panel and Pseudo-Panel Estimation of Cross-Sectional and Time Series Elasticities of Food Consumption: The Case of American and Polish Data

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    The problem addressed in this article is the bias to income and expenditure elasticities estimated on pseudo-panel data caused by measurement error and unobserved heterogeneity. We gauge empirically these biases by comparing cross-sectional, pseudo-panel and true panel data from both Polish and American expenditure surveys. Our results suggest that unobserved heterogeneity imparts a downward bias to cross-section estimates of income elasticities of at-home food expenditures and an upward bias to estimates of income elasticities of away-from-home food expenditures. "Within" and first-difference estimators suffer less bias, but only if the effects of measurement error are accounted for with instrumental variables.individual and grouped data; unobserved heterogeneity; AIDS model

    Tensor and Matrix Inversions with Applications

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    Higher order tensor inversion is possible for even order. We have shown that a tensor group endowed with the Einstein (contracted) product is isomorphic to the general linear group of degree nn. With the isomorphic group structures, we derived new tensor decompositions which we have shown to be related to the well-known canonical polyadic decomposition and multilinear SVD. Moreover, within this group structure framework, multilinear systems are derived, specifically, for solving high dimensional PDEs and large discrete quantum models. We also address multilinear systems which do not fit the framework in the least-squares sense, that is, when the tensor has an odd number of modes or when the tensor has distinct dimensions in each modes. With the notion of tensor inversion, multilinear systems are solvable. Numerically we solve multilinear systems using iterative techniques, namely biconjugate gradient and Jacobi methods in tensor format

    Weak and strong wave turbulence spectra for elastic thin plate

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    Variety of statistically steady energy spectra in elastic wave turbulence have been reported in numerical simulations, experiments, and theoretical studies. Focusing on the energy levels of the system, we have performed direct numerical simulations according to the F\"{o}ppl--von K\'{a}rm\'{a}n equation, and successfully reproduced the variability of the energy spectra by changing the magnitude of external force systematically. When the total energies in wave fields are small, the energy spectra are close to a statistically steady solution of the kinetic equation in the weak turbulence theory. On the other hand, in large-energy wave fields, another self-similar spectrum is found. Coexistence of the weakly nonlinear spectrum in large wavenumbers and the strongly nonlinear spectrum in small wavenumbers are also found in moderate energy wave fields.Comment: 5 pages, 3 figure

    Least-Squares, Continuous Sensitivity Analysis for Nonlinear Fluid-Structure Interaction

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    A least-squares, continuous sensitivity analysis method is developed for transient aeroelastic gust response problems to support computationally efficient analysis and optimization of aeroelastic design problems. A key distinction between the local and total derivative forms of the sensitivity system is introduced. The continuous sensitivity equations and sensitivity boundary conditions are derived in local derivative form which is shown to be superior for several applications. The analysis and sensitivity problems are both posed in a first-order form which is amenable to a solution using the least-squares finite element method. Several example and validation problems are presented and solved, including elasticity, fluid, and fluid-structure interaction problems. Significant contributions of the research include the first sensitivity analysis of nonlinear transient gust response, a local derivative formulation for shape variation that requires parameterizing only the boundary, and statement of sufficient conditions for using nonlinear black box software to solve the sensitivity equations. Promising paths for future investigation are presented and discussed

    The exponentiated Hencky strain energy in modelling tire derived material for moderately large deformations

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    This work presents a hyper-viscoelastic model based on the Hencky-logarithmic strain tensor to model the response of a Tire Derived Material (TDM) undergoing moderately large deformations. TDM is a composite made by cold forging a mix of rubber fibers and grains, obtained by grinding scrap tires, and polyurethane binder. The mechanical properties are highly influenced by the presence of voids associated with the granular composition and low tensile strength due to the weak connection at the grain-matrix interface. For these reasons, TDM use is restricted to applications concerning a limited range of deformations. Experimental tests show that a central feature of the response is connected to highly nonlinear behavior of the material under volumetric deformation which conventional hyperelastic models fail in predicting. The strain energy function presented here is a variant of the exponentiated Hencky strain energy proposed by Neff et al., which for moderate strains is as good as the quadratic Hencky model and in the large strain region improves several important features from a mathematical point of view. The proposed form of the exponentiated Hencky energy possesses a set of parameters uniquely determined in the infinitesimal strain regime and an orthogonal set of parameters to determine the nonlinear response. The hyperelastic model is additionally incorporated in a finite deformation viscoelasticity framework that accounts for the two main dissipation mechanisms in TDMs, one at the microscale level and one at the macroscale level. The model is capable of predicting different deformation modes in a certain range of frequency and amplitude with a unique set of parameters with most of them having a clear physical meaning. Moreover, by comparing the predictions from the proposed constitutive model with experimental data we conclude that the new constitutive model gives accurate prediction

    Panel and Pseudo-Panel Estimation of Cross-Sectional and Time Series Elasticities of Food Consumption: The Case of American and Polish Data

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    12 p.International audienceThe problem addressed in this article is the bias to income and expenditure elasticities estimated on pseudo-panel data caused by measurement error and unobserved heterogeneity. We gauge empirically these biases by comparing cross-sectional, pseudo-panel and true panel data from both Polish and American expenditure surveys. Our results suggest that unobserved heterogeneity imparts a downward bias to cross-section estimates of income elasticities of at-home food expenditures and an upward bias to estimates of income elasticities of away-from-home food expenditures. "Within" and first-difference estimators suffer less bias, but only if the effects of measurement error are accounted for with instrumental variables

    Cumulative reports and publications through December 31, 1988

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    This document contains a complete list of ICASE Reports. Since ICASE Reports are intended to be preprints of articles that will appear in journals or conference proceedings, the published reference is included when it is available
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