The Experts below are selected from a list of 81 Experts worldwide ranked by ideXlab platform

Vesa Klumpp - One of the best experts on this subject based on the ideXlab platform.

  • MFI - Localized Cumulative Distributions and a multivariate generalization of the Cramér-von Mises distance
    2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2008
    Co-Authors: Uwe D. Hanebeck, Vesa Klumpp
    Abstract:

    This paper is concerned with distances for comparing multivariate Random Vectors with a special focus on the case that at least one of the Random Vectors is of discrete type, i.e., assumes values from a discrete set only. The first contribution is a new type of characterization of multivariate Random quantities, the so called localized cumulative distribution (LCD) that, in contrast to the conventional definition of a cumulative distribution, is unique and symmetric. Based on the LCDs of the Random Vectors under consideration, the second contribution is the definition of generalized distance measures that are suitable for the multivariate case. These distances are used for both analysis and synthesis purposes. Analysis is concerned with assessing whether a given sample stems from a given Continuous distribution. Synthesis is concerned with both density estimation, i.e., calculating a suitable Continuous approximation of a given sample, and density discretization, i.e., approximation of a given Continuous Random Vector by a discrete one.

  • Localized Cumulative Distributions and a multivariate generalization of the Cramér-von Mises distance
    2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2008
    Co-Authors: Uwe D. Hanebeck, Vesa Klumpp
    Abstract:

    This paper is concerned with distances for comparing multivariate Random Vectors with a special focus on the case that at least one of the Random Vectors is of discrete type, i.e., assumes values from a discrete set only. The first contribution is a new type of characterization of multivariate Random quantities, the so called localized cumulative distribution (LCD) that, in contrast to the conventional definition of a cumulative distribution, is unique and symmetric. Based on the LCDs of the Random Vectors under consideration, the second contribution is the definition of generalized distance measures that are suitable for the multivariate case. These distances are used for both analysis and synthesis purposes. Analysis is concerned with assessing whether a given sample stems from a given Continuous distribution. Synthesis is concerned with both density estimation, i.e., calculating a suitable Continuous approximation of a given sample, and density discretization, i.e., approximation of a given Continuous Random Vector by a discrete one.

Uwe D. Hanebeck - One of the best experts on this subject based on the ideXlab platform.

  • MFI - Localized Cumulative Distributions and a multivariate generalization of the Cramér-von Mises distance
    2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2008
    Co-Authors: Uwe D. Hanebeck, Vesa Klumpp
    Abstract:

    This paper is concerned with distances for comparing multivariate Random Vectors with a special focus on the case that at least one of the Random Vectors is of discrete type, i.e., assumes values from a discrete set only. The first contribution is a new type of characterization of multivariate Random quantities, the so called localized cumulative distribution (LCD) that, in contrast to the conventional definition of a cumulative distribution, is unique and symmetric. Based on the LCDs of the Random Vectors under consideration, the second contribution is the definition of generalized distance measures that are suitable for the multivariate case. These distances are used for both analysis and synthesis purposes. Analysis is concerned with assessing whether a given sample stems from a given Continuous distribution. Synthesis is concerned with both density estimation, i.e., calculating a suitable Continuous approximation of a given sample, and density discretization, i.e., approximation of a given Continuous Random Vector by a discrete one.

  • Localized Cumulative Distributions and a multivariate generalization of the Cramér-von Mises distance
    2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2008
    Co-Authors: Uwe D. Hanebeck, Vesa Klumpp
    Abstract:

    This paper is concerned with distances for comparing multivariate Random Vectors with a special focus on the case that at least one of the Random Vectors is of discrete type, i.e., assumes values from a discrete set only. The first contribution is a new type of characterization of multivariate Random quantities, the so called localized cumulative distribution (LCD) that, in contrast to the conventional definition of a cumulative distribution, is unique and symmetric. Based on the LCDs of the Random Vectors under consideration, the second contribution is the definition of generalized distance measures that are suitable for the multivariate case. These distances are used for both analysis and synthesis purposes. Analysis is concerned with assessing whether a given sample stems from a given Continuous distribution. Synthesis is concerned with both density estimation, i.e., calculating a suitable Continuous approximation of a given sample, and density discretization, i.e., approximation of a given Continuous Random Vector by a discrete one.

Ivan Kojadinovic - One of the best experts on this subject based on the ideXlab platform.

  • Some copula inference procedures adapted to the presence of ties
    Computational Statistics & Data Analysis, 2017
    Co-Authors: Ivan Kojadinovic
    Abstract:

    When modeling the distribution of a multivariate Continuous Random Vector using the so-called copula approach, it is not uncommon to have ties in the coordinate samples of the available data because of rounding or lack of measurement precision. Yet, the vast majority of existing inference procedures on the underlying copula were both theoretically derived and practically implemented under the assumption of no ties. Applying them nonetheless can lead to strongly biased results. Some of the existing statistical tests can however be adapted to provide meaningful results in the presence of ties. It is the case of some tests of exchangeability, radial symmetry, extreme-value dependence and goodness of fit. Detailed algorithms for computing approximate p-values for the modified tests are provided and their finite-sample behaviors are empirically investigated through extensive Monte Carlo experiments. An illustration on a real-world insurance data set concludes the work.

  • Some copula inference procedures adapted to the presence of ties
    arXiv: Methodology, 2016
    Co-Authors: Ivan Kojadinovic
    Abstract:

    When modeling the distribution of a multivariate Continuous Random Vector using the so-called \emph{copula approach}, it is not uncommon to have ties in the coordinate samples of the available data because of rounding or lack of measurement precision. Yet, the vast majority of existing inference procedures on the underlying copula were both theoretically derived and practically implemented under the assumption of no ties. Applying them nonetheless can lead to strongly biased results. Some of the existing statistical tests can however be adapted to provide meaningful results in the presence of ties. It is the case of some tests of exchangeability, radial symmetry, extreme-value dependence and goodness of fit. Detailed algorithms for computing approximate p-values for the modified tests are provided and their finite-sample behaviors are empirically investigated through extensive Monte Carlo experiments. An illustration on a real-world insurance data set concludes the work.

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

  • a discrete variance separation estimation to the joint entropy of Continuous Random Vector
    Mathematics in Practice and Theory, 2010
    Co-Authors: Wang Renming
    Abstract:

    A new method named Discrete Variance Separation(DVS) is proposed in this paper to estimate the joint entropy of Continuous Random Vector.This method can be separated into two steps.The first step referred to as"variance separation"divides the joint entropies into standard entropy and the sum of the logarithm of standard deviations in order to prevent the curse of dimension.The second step called"discretization"estimates the standard entropy,which discretizes Continuous Random Vectors by the best partition numbers of their elements so that bypasses the estimation of joint density.Simulated experiments show that results from this new method approach the theoretic values with high accuracy and computation complexity.

Haikady N. Nagaraja - One of the best experts on this subject based on the ideXlab platform.

  • Distribution of concomitants of order statistics and their order statistics
    Journal of Statistical Planning and Inference, 2009
    Co-Authors: Qinying He, Haikady N. Nagaraja
    Abstract:

    Abstract For a Random sample of size n from an absolutely Continuous Random Vector ( X , Y ) , let Y i : n be i th Y -order statistic and Y [ j : n ] be the Y -concomitant of X j : n . We determine the joint pdf of Y i : n and Y [ j : n ] for all i , j = 1 to n , and establish some symmetry properties of the joint distribution for symmetric populations. We discuss the uses of the joint distribution in the computation of moments and probabilities of various ranks for Y [ j : n ] . We also show how our results can be used to determine the expected cost of mismatch in broken bivariate samples and approximate the first two moments of the ratios of linear functions of Y i : n and Y [ j : n ] . For the bivariate normal case, we compute the expectations of the product of Y i : n and Y [ i : n ] for n = 2 to 8 for selected values of the correlation coefficient and illustrate their uses.