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Ignace Van De Woestyne - One of the best experts on this subject based on the ideXlab platform.

  • Geometric Representation of the mean variance skewness portfolio frontier based upon the shortage function
    European Journal of Operational Research, 2011
    Co-Authors: Kristiaan Kerstens, Amine Mounir, Ignace Van De Woestyne
    Abstract:

    The literature suggests that investors prefer portfolios based on mean, variance and skewness rather than portfolios based on mean-variance (MV) criteria solely. Furthermore, a small variety of methods have been proposed to determine meanvariance-skewness (MVS) optimal portfolios. Recently, the shortage function has been introduced as a measure of efficiency, allowing to characterize MVS optimal portfolios using non-parametric mathematical programming tools. While tracing the MV portfolio frontier has become trivial, the Geometric Representation of the MVS frontier is an open challenge. A hitherto unnoticed advantage of the shortage function is that it allows to Geometrically represent the MVS portfolio frontier. The purpose of this contribution is to systematically develop Geometric Representations of the MVS portfolio frontier using the shortage function and related approaches.

  • Geometric Representation of the mean–variance–skewness portfolio frontier based upon the shortage function
    European Journal of Operational Research, 2011
    Co-Authors: Kristiaan Kerstens, Amine Mounir, Ignace Van De Woestyne
    Abstract:

    The literature suggests that investors prefer portfolios based on mean, variance and skewness rather than portfolios based on mean-variance (MV) criteria solely. Furthermore, a small variety of methods have been proposed to determine meanvariance-skewness (MVS) optimal portfolios. Recently, the shortage function has been introduced as a measure of efficiency, allowing to characterize MVS optimal portfolios using non-parametric mathematical programming tools. While tracing the MV portfolio frontier has become trivial, the Geometric Representation of the MVS frontier is an open challenge. A hitherto unnoticed advantage of the shortage function is that it allows to Geometrically represent the MVS portfolio frontier. The purpose of this contribution is to systematically develop Geometric Representations of the MVS portfolio frontier using the shortage function and related approaches.

Kristiaan Kerstens - One of the best experts on this subject based on the ideXlab platform.

  • Geometric Representation of the mean variance skewness portfolio frontier based upon the shortage function
    European Journal of Operational Research, 2011
    Co-Authors: Kristiaan Kerstens, Amine Mounir, Ignace Van De Woestyne
    Abstract:

    The literature suggests that investors prefer portfolios based on mean, variance and skewness rather than portfolios based on mean-variance (MV) criteria solely. Furthermore, a small variety of methods have been proposed to determine meanvariance-skewness (MVS) optimal portfolios. Recently, the shortage function has been introduced as a measure of efficiency, allowing to characterize MVS optimal portfolios using non-parametric mathematical programming tools. While tracing the MV portfolio frontier has become trivial, the Geometric Representation of the MVS frontier is an open challenge. A hitherto unnoticed advantage of the shortage function is that it allows to Geometrically represent the MVS portfolio frontier. The purpose of this contribution is to systematically develop Geometric Representations of the MVS portfolio frontier using the shortage function and related approaches.

  • Geometric Representation of the mean–variance–skewness portfolio frontier based upon the shortage function
    European Journal of Operational Research, 2011
    Co-Authors: Kristiaan Kerstens, Amine Mounir, Ignace Van De Woestyne
    Abstract:

    The literature suggests that investors prefer portfolios based on mean, variance and skewness rather than portfolios based on mean-variance (MV) criteria solely. Furthermore, a small variety of methods have been proposed to determine meanvariance-skewness (MVS) optimal portfolios. Recently, the shortage function has been introduced as a measure of efficiency, allowing to characterize MVS optimal portfolios using non-parametric mathematical programming tools. While tracing the MV portfolio frontier has become trivial, the Geometric Representation of the MVS frontier is an open challenge. A hitherto unnoticed advantage of the shortage function is that it allows to Geometrically represent the MVS portfolio frontier. The purpose of this contribution is to systematically develop Geometric Representations of the MVS portfolio frontier using the shortage function and related approaches.

  • Geometric Representation of the Mean-Variance-Skewness Porfolio Frontier Based upon the Shortage Function
    2007
    Co-Authors: Kristiaan Kerstens
    Abstract:

    The literature suggests that investors prefer portfolios based on mean, variance and skewness rather than portfolios based on mean-variance (MV) criteria solely. Furthermore, a small variety of methods have been proposed to determine mean-variance-skewness (MVS) optimal portfolios. Recently, the shortage function has been introduced as a measure of efficiency, allowing to characterize MVS optimalportfolios using non-parametric mathematical programming tools. While tracing the MV portfolio frontier has become trivial, the Geometric Representation of the MVS frontier is an open challenge. A hitherto unnoticed advantage of the shortage function is that it allows to Geometrically represent the MVS portfolio frontier. The purpose of this contribution is to systematically develop Geometric Representations of the MVS portfolio frontier using the shortage function and related approaches.(This abstract was borrowed from another version of this item.)

Amine Mounir - One of the best experts on this subject based on the ideXlab platform.

  • Geometric Representation of the mean variance skewness portfolio frontier based upon the shortage function
    European Journal of Operational Research, 2011
    Co-Authors: Kristiaan Kerstens, Amine Mounir, Ignace Van De Woestyne
    Abstract:

    The literature suggests that investors prefer portfolios based on mean, variance and skewness rather than portfolios based on mean-variance (MV) criteria solely. Furthermore, a small variety of methods have been proposed to determine meanvariance-skewness (MVS) optimal portfolios. Recently, the shortage function has been introduced as a measure of efficiency, allowing to characterize MVS optimal portfolios using non-parametric mathematical programming tools. While tracing the MV portfolio frontier has become trivial, the Geometric Representation of the MVS frontier is an open challenge. A hitherto unnoticed advantage of the shortage function is that it allows to Geometrically represent the MVS portfolio frontier. The purpose of this contribution is to systematically develop Geometric Representations of the MVS portfolio frontier using the shortage function and related approaches.

  • Geometric Representation of the mean–variance–skewness portfolio frontier based upon the shortage function
    European Journal of Operational Research, 2011
    Co-Authors: Kristiaan Kerstens, Amine Mounir, Ignace Van De Woestyne
    Abstract:

    The literature suggests that investors prefer portfolios based on mean, variance and skewness rather than portfolios based on mean-variance (MV) criteria solely. Furthermore, a small variety of methods have been proposed to determine meanvariance-skewness (MVS) optimal portfolios. Recently, the shortage function has been introduced as a measure of efficiency, allowing to characterize MVS optimal portfolios using non-parametric mathematical programming tools. While tracing the MV portfolio frontier has become trivial, the Geometric Representation of the MVS frontier is an open challenge. A hitherto unnoticed advantage of the shortage function is that it allows to Geometrically represent the MVS portfolio frontier. The purpose of this contribution is to systematically develop Geometric Representations of the MVS portfolio frontier using the shortage function and related approaches.

Amnon Neeman - One of the best experts on this subject based on the ideXlab platform.

  • Geometric Representation of high dimension low sample size data
    Journal of The Royal Statistical Society Series B-statistical Methodology, 2005
    Co-Authors: Peter Hall, J S Marron, Amnon Neeman
    Abstract:

    High dimension, low sample size data are emerging in various areas of science. We find a common structure underlying many such data sets by using a non-standard type of asymptotics: the dimension tends to ∞ while the sample size is fixed. Our analysis shows a tendency for the data to lie deterministically at the vertices of a regular simplex. Essentially all the randomness in the data appears only as a random rotation of this simplex. This Geometric Representation is used to obtain several new statistical insights. Copyright 2005 Royal Statistical Society.

  • Geometric Representation of high dimension, low sample size data
    Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2005
    Co-Authors: Peter Hall, J S Marron, Amnon Neeman
    Abstract:

    High dimension, low sample size data are emerging in various areas of science. We find a common structure underlying many such data sets by using a non-standard type of asymptotics: the dimension tends to ∞ while the sample size is fixed. Our analysis shows a tendency for the data to lie deterministically at the vertices of a regular simplex. Essentially all the randomness in the data appears only as a random rotation of this simplex. This Geometric Representation is used to obtain several new statistical insights. Copyright 2005 Royal Statistical Society.

Ning Wang - One of the best experts on this subject based on the ideXlab platform.

  • an enhanced chromosome encoding and morphological Representation of geometry for structural topology optimization using ga
    Congress on Evolutionary Computation, 2007
    Co-Authors: Ning Wang
    Abstract:

    The structural topology optimization approach can be used to generate the structural design for some desired input-output (force-deflection) requirements. Optimization methods based on genetic algorithms (GA) have recently been demonstrated to have the potential for overcoming the problems associated with gradient-based methods. The success of the GA depends, to a large extent, on the structural geometry Representation scheme used. In this work, some enhancements are incorporated into the recently developed morphological Geometric Representation scheme coupled with a GA. Based on the morphology of living creatures, a Geometric Representation scheme had earlier been developed that works by specifying a skeleton which defines the underlying topology/connectivity of a structural continuum together with segments of material surrounding the skeleton. In this work, the flexibility to turn on or off parts of the skeleton is integrated into the scheme. This improves the variability of topological and shape characteristics in the evolutionary process and enhances the Representation's versatility. The methodology is tested by solving a multicriterion 'target matching' problem : a simulated topology optimization problem where a 'target' geometry is first created and predefined as the optimum solution, and design solutions are evolved to converge towards this target shape.