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Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article ¨ Gruss-Type Bounds for the Covariance of Transformed Random Variables | Hindawi Publishing Corporation Journal of Inequalities and Applications Volume 2010 Article ID 619423 10 pages doi 10.1155 2010 619423 Research Article Griiss-Type Bounds for the Covariance of Transformed Random Variables Martin Egozcue 1 2 Luis Fuentes García 3 Wing-Keung Wong 4 and RiCardas Zitikis5 1 Department of Economics University of Montevideo Montevideo 11600 Uruguay 2 Accounting and Finance Department Norte Construcciones Punta del Este 20100 Uruguay 3 Departamento de Metodos Matematicos e de Representation Escola Tecnica Superior de Enxefieiros de Caminos Canais e Portos Universidade da Coruna 15001 A Coruna Spain 4 Department of Economics Institute for Computational Mathematics Hong Kong Baptist University Kowloon Tong Hong Kong 5 Department of Statistical and Actuarial Sciences University of Western Ontario London ON Canada N6A 5B7 Correspondence should be addressed to Ricardas Zitikis zitikis@stats.uwo.ca Received 9 November 2009 Revised 28 February 2010 Accepted 16 March 2010 Academic Editor Soo Hak Sung Copyright 2010 Martin Egozcue et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. A number of problems in Economics Finance Information Theory Insurance and generally in decision making under uncertainty rely on estimates of the covariance between transformed random variables which can for example be losses risks incomes financial returns and so forth. Several avenues relying on inequalities for analyzing the covariance are available in the literature bearing the names of Chebyshev Gruss Hoeffding Kantorovich and others. In the present paper we sharpen the upper bound of a Gruss-type covariance inequality by incorporating a notion of quadrant dependence between random variables and also utilizing the idea of constraining the means of the random variables. 1. Introduction Analyzing and .
