ON THE CHOICE OF SYNTHETIC MEASURES FOR ASSESSING ECONOMIC EFFECTS

• Authors: Binderman Zbigniew , Bolesław Borkowski , Koszela Grzegorz , Kozera Ryszard , Szczesny Wiesław
• Languages of publication: EN

Abstracts

EN

Multidimensional analysis uses various measures for assessing economic effects. However, no single synthetic measure, regardless how popular, can give a satisfactory solution to the above problem. In general, various approaches of combining measures can lead to stable outcomes. Nevertheless, when combining "weak" classifiers one can obtain inevitably poorer classification. We propose here a new approach to construct doubly synthetic measures. The main goal of this work is to analyse the influence these new synthetic measures on the ranking of multidimensional objects.

Keywords

EN

multidimensional analysis; synthetic measure; ranking methods

• Publisher: Katedra Ekonometrii i Statystyki, Szkoła Główna Gospodarstwa Wiejskiego w Warszawie
• Journal: Metody Ilościowe w Badaniach Ekonomicznych
• Year: 2017
• Volume: 18
• Issue: 1
• Pages: 7-17

Contributors

author

Binderman Zbigniew

• Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences – SGGW, Poland

author

Bolesław Borkowski

• Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences – SGGW, Poland

author

Koszela Grzegorz

• Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences – SGGW, Poland

author

Kozera Ryszard

• Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences – SGGW, Poland

author

Szczesny Wiesław

• Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences – SGGW, Poland

References

• Aczel A.D. (1989) Complete Business Statistics. Richard D. Irwin Inc. Homewood.
• Binderman A. (2006) Klasyfikacja obiektów oparta na dwóch wzorcach, EiOGŻ, Zeszyty Naukowe SGGW, nr 60, Warszawa, 25-37„in Polish” .
• Binderman, Z., Borkowski B., Szczesny W. (2012) Radar coefficient of concentration, Quantitative Methods in Economics Vol. XIII, nr 2, 7-21.
• Binderman Z., Borkowski B., Szczesny W. (2013) Synthetic radar measures that factor in weights of features, Quantitative Methods in Economics Vol. XIV, No. 1, 37– 52.
• Breiman L. (1998) Bias-variance, regularization, instability and stabilization. C. M. Bishop, red., Neural Networks and Machine Learning, Springer-Verlag, 27–56.
• Breiman L., (1996) Arcing Classifiers, Technical Report 460, Department of Statistics, University of California, CA, USA, February.
• Breiman L. (1994) Bagging predictors, Technical Report 420, Department of Statistics, University of California, CA, USA.
• Gatnar E., Walesiak M. (2009) Statystyczna analiza danych z wykorzystaniem programu R, PWN, Warszawa „in Polish”.
• Hair J.F. et al. (1995) Multivariate Data Analysis with Readings. Prentice Hall International, Ltd. London
• Hellwig Z. (1968) Zastosowanie metody taksonomicznej do typologicznego podziału krajów ze względu na poziom ich rozwoju oraz zasoby i strukturę kwalifikowanych kadr, Przegląd Stat., 4.
• Hwang. C.L., Yoon K. (1981) Multiple attribute decision making: Methods and Applications, Springer-Verlag, New York.
• Jackson D.H. (1970) The stability of classifications of binary attribute data, T.R., Cornell University.
• Jackson D.H. (1969) Comparison of classification, Academic Press.
• Jackson D.H. (1969a) Automatic classifications and information retrieval, Cambridge University.
• Mitrinović Dragoslav S. (1993) Classical and New Inequalities in Analysis. Springer – Science + Bisiness Media Dordrecht.
• Morrison Donald F. (1990) Mulivariate Statistical Methods. Third Edition. McGraw-Hill,. New York
• Kukuła K. (2000) Metoda unitaryzacji zerowanej, PWN, Warszawa „in Polish”.