EconStor Community: University of Cologne, Institute of Econometrics and Statistics
http://hdl.handle.net/10419/230
University of Cologne, Institute of Econometrics and Statistics2016-06-26T19:12:59ZGeometrical framework for robust portfolio optimization
http://hdl.handle.net/10419/102645
Title: Geometrical framework for robust portfolio optimization
Authors: Bazovkin, Pavel
Abstract: We consider a vector-valued multivariate risk measure that depends on the user's profile given by the user's utility. It is constructed on the basis of weighted-mean trimmed regions and represents the solution of an optimization problem. The key feature of this measure is convexity. We apply the measure to the portfolio selection problem, employing different measures of performance as objective functions in a common geometrical framework.2014-01-01T00:00:00ZConsidering the extremely poor: Multidimensional poverty measurement for Germany
http://hdl.handle.net/10419/102646
Title: Considering the extremely poor: Multidimensional poverty measurement for Germany
Authors: Nowak, Daniel; Scheicher, Christoph
Abstract: This paper applies the Alkire and Foster (2011) index of multidimensional poverty to German data. This is done with respect to the politically most important dimensions of poverty mentioned in the German federal government's report on poverty and wealth. Additionally, a modification of the identification step of the Alkire-Foster index is proposed to guarantee that individuals, who are extremely poor in only few dimensions, are not omitted by the index.2014-01-01T00:00:00ZA Jarque-Bera test for sphericity of a large-dimensional covariance matrix
http://hdl.handle.net/10419/73684
Title: A Jarque-Bera test for sphericity of a large-dimensional covariance matrix
Authors: Glombek, Konstantin
Abstract: This article provides a new test for sphericity of the covariance matrix of a d-dimensional multinormal population X ∼ Nd(µ,Σ). This test is applicable if the sample size, n + 1, and d both go to infinity while d/n → y ∈ (0,∞), provided that the limits of tr(Σk)/d, k = 1,...,8, are finite. The main idea of this test is to check whether the empirical eigenvalue distribution of a suitably standardized sample covariance matrix obeys the semicircle law. Due to similarities of the semicircle law to the normal distribution, the proposed test statistic is of the type of the Jarque-Bera test statistic. Simulation results show that the new sphericity test outperforms the tests from the current literature for certain local alternatives if y is small.2013-01-01T00:00:00ZFast nonparametric classification based on data depth
http://hdl.handle.net/10419/67614
Title: Fast nonparametric classification based on data depth
Authors: Lange, Tatjana; Mosler, Karl; Mozharovskyi, Pavlo
Abstract: A new procedure, called DD-procedure, is developed to solve the problem of classifying d-dimensional objects into q Ï 2 classes. The procedure is completely nonparametric; it uses q-dimensional depth plots and a very efficient algorithm for discrimination analysis in the depth space [0, 1]q . Specifically, the depth is the zonoid depth, and the algorithm is the procedure. In case of more than two classes several binary classifications are performed and a majority rule is applied. Special treatments are discussed for outsiders, that is, data having zero depth vector. The DD-classifier is applied to simulated as well as real data, and the results are compared with those of similar procedures that have been recently proposed. In most cases the new procedure has comparable error rates, but is much faster than other classification approaches, including the SVM.2012-01-01T00:00:00Z