The Experts below are selected from a list of 93369 Experts worldwide ranked by ideXlab platform
Martin H Y Yick - One of the best experts on this subject based on the ideXlab platform.
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does tax Convexity matter for risk a dynamic study of tax asymmetry and equity beta
Review of Quantitative Finance and Accounting, 2013Co-Authors: Martin H Y YickAbstract:The purpose of this study is to explore the effect of tax Convexity on firms’ market risk, where tax Convexity measures the progressivity of firms’ tax function. We examine the relation between equity beta and tax Convexity based on a standard contingent-claims model, in which firms face nonlinear tax schedules. We verify that in the presence of default and growth options, the effect of tax Convexity on beta is significant and depends on several countervailing forces. Tax Convexity has a direct, positive effect on beta, as well as two indirect countereffects through default and growth options. The overall effect is ambiguous and quantitatively significant. As asymmetric tax schedules are used in most countries, assuming a linear tax schedule in the valuation framework may misestimate beta and thus fail to assess risk accurately. Our theoretical model shows that tax Convexity should be taken into consideration when estimating equity beta.
Paul L. Rosin - One of the best experts on this subject based on the ideXlab platform.
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Probabilistic Convexity measure
IET Image Processing, 2007Co-Authors: Paul L. Rosin, Jovisa ZunicAbstract:In order to improve the effectiveness of shape-based classification, there is an ongoing interest in creating new shape descriptors or creating new measures for descriptors that are already defined and used in shape classification tasks. Convexity is one of the most widely used shape descriptors and also one of the most studied in the literature. There are already several defined Convexity measures. The most standard one comes from the comparison between a given shape and its convex hull. There are also some non-trivial approaches. Here, we define a new measure for shape Convexity. It incorporates both area-based and boundary-based information, and in accordance with this it is more sensitive to boundary defects than exclusively area-based Convexity measures. The new measure has several desirable properties and it is invariant under similarity transformations. When compared with Convexity measures that trivially follow from the comparison between a measured shape and its convex hull then the new Convexity measure also shows some advantages, particularly for shapes with holes.
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A new Convexity measure for polygons
IEEE transactions on pattern analysis and machine intelligence, 2004Co-Authors: Jovisa Zunic, Paul L. RosinAbstract:Convexity estimators are commonly used in the analysis of shape. In this paper, we define and evaluate a new Convexity measure for planar regions bounded by polygons. The new Convexity measure can be understood as a "boundary-based" measure and in accordance with this it is more sensitive to measured boundary defects than the so called "area-based" Convexity measures. When compared with the Convexity measure defined as the ratio between the Euclidean perimeter of the convex hull of the measured shape and the Euclidean perimeter of the measured shape then the new Convexity measure also shows some advantages-particularly for shapes with holes. The new Convexity measure has the following desirable properties: 1) the estimated Convexity is always a number from (0, 1], 2) the estimated Convexity is I if and only if the measured shape is convex, 3) there are shapes whose estimated Convexity is arbitrarily close to 0, 4) the new Convexity measure is invariant under similarity transformations, and 5) there is a simple and fast procedure for computing the new Convexity measure.
Jovisa Zunic - One of the best experts on this subject based on the ideXlab platform.
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Probabilistic Convexity measure
IET Image Processing, 2007Co-Authors: Paul L. Rosin, Jovisa ZunicAbstract:In order to improve the effectiveness of shape-based classification, there is an ongoing interest in creating new shape descriptors or creating new measures for descriptors that are already defined and used in shape classification tasks. Convexity is one of the most widely used shape descriptors and also one of the most studied in the literature. There are already several defined Convexity measures. The most standard one comes from the comparison between a given shape and its convex hull. There are also some non-trivial approaches. Here, we define a new measure for shape Convexity. It incorporates both area-based and boundary-based information, and in accordance with this it is more sensitive to boundary defects than exclusively area-based Convexity measures. The new measure has several desirable properties and it is invariant under similarity transformations. When compared with Convexity measures that trivially follow from the comparison between a measured shape and its convex hull then the new Convexity measure also shows some advantages, particularly for shapes with holes.
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A new Convexity measure for polygons
IEEE transactions on pattern analysis and machine intelligence, 2004Co-Authors: Jovisa Zunic, Paul L. RosinAbstract:Convexity estimators are commonly used in the analysis of shape. In this paper, we define and evaluate a new Convexity measure for planar regions bounded by polygons. The new Convexity measure can be understood as a "boundary-based" measure and in accordance with this it is more sensitive to measured boundary defects than the so called "area-based" Convexity measures. When compared with the Convexity measure defined as the ratio between the Euclidean perimeter of the convex hull of the measured shape and the Euclidean perimeter of the measured shape then the new Convexity measure also shows some advantages-particularly for shapes with holes. The new Convexity measure has the following desirable properties: 1) the estimated Convexity is always a number from (0, 1], 2) the estimated Convexity is I if and only if the measured shape is convex, 3) there are shapes whose estimated Convexity is arbitrarily close to 0, 4) the new Convexity measure is invariant under similarity transformations, and 5) there is a simple and fast procedure for computing the new Convexity measure.
George A. Anastassiou - One of the best experts on this subject based on the ideXlab platform.
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Convex Probabilistic Wavelet Like Approximation
Intelligent Mathematics: Computational Analysis, 2011Co-Authors: George A. AnastassiouAbstract:Continuous functions are approximated by wavelet like operators. These preserve Convexity and r-Convexity and transform continuous probability distribution functions into probability distribution functions at the same time preserving certain Convexity conditions. The degree of this approximation is estimated by presented Jackson type inequalities.
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Convex and coconvex-probabilistic wavelet approximation
Stochastic Analysis and Applications, 1992Co-Authors: George A. AnastassiouAbstract:Continuous functions are approximated by wavelet operators. These preserve Convexity and r-Convexity and transform continuous probability distribution functions into probability distribution functions at the same time preserving certain Convexity conditions. The degree of this approximation is estimated by establishing some Jackson type inequalities
Chandrasekaran Venkat - One of the best experts on this subject based on the ideXlab platform.
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Terracini Convexity
2020Co-Authors: Saunderson James, Chandrasekaran VenkatAbstract:We present a generalization of the notion of neighborliness to non-polyhedral convex cones. Although a definition of neighborliness is available in the non-polyhedral case in the literature, it is fairly restrictive as it requires all the low-dimensional faces to be polyhedral. Our approach is more flexible and includes, for example, the cone of positive-semidefinite matrices as a special case (this cone is not neighborly in general). We term our generalization Terracini Convexity due to its conceptual similarity with the conclusion of Terracini's lemma from algebraic geometry. Polyhedral cones are Terracini convex if and only if they are neighborly. More broadly, we derive many families of non-polyhedral Terracini convex cones based on neighborly cones, linear images of cones of positive semidefinite matrices, and derivative relaxations of Terracini convex hyperbolicity cones. As a demonstration of the utility of our framework in the non-polyhedral case, we give a characterization based on Terracini Convexity of the tightness of semidefinite relaxations for certain inverse problems.Comment: 35 pages, 1 figur
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Terracini Convexity
2020Co-Authors: Saunderson James, Chandrasekaran VenkatAbstract:We present a generalization of the notion of neighborliness to non-polyhedral convex cones. Although a definition of neighborliness is available in the non-polyhedral case in the literature, it is fairly restrictive as it requires all the low-dimensional faces to be polyhedral. Our approach is more flexible and includes, for example, the cone of positive-semidefinite matrices as a special case (this cone is not neighborly in general). We term our generalization Terracini Convexity due to its conceptual similarity with the conclusion of Terracini's lemma from algebraic geometry. Polyhedral cones are Terracini convex if and only if they are neighborly. More broadly, we derive many families of non-polyhedral Terracini convex cones based on neighborly cones, linear images of cones of positive semidefinite matrices, and derivative relaxations of Terracini convex hyperbolicity cones. As a demonstration of the utility of our framework in the non-polyhedral case, we give a characterization based on Terracini Convexity of the tightness of semidefinite relaxations for certain inverse problems
