- Author
- Blatná Dagmar (University of Economics, Prague, Czech Republic)
- Title
- European Countries Analysis Using Robust Regression Methods
Analiza krajów europejskich za pomocą metod regresji odpornej - Source
- Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 2009, nr 66, s. 21-31, tab., rys., bibliogr. 13 poz.
Research Papers of Wrocław University of Economics - Issue title
- Towards Information-Based Welfare Society
- Keyword
- Odporne metody statystyczne, Wskaźniki ekonomiczne, Modele regresji
Robust statistical methods, Economic indicators, Regression models - Note
- summ., streszcz.
- Abstract
- Kraje europejskie scharakteryzowane za pomocą wskaźników ekonomicznych, takich jak zatrudnienie, innowacje, badania naukowe, technologia, analizowane są za pomocą regresji odpornej. W pracy wykazano, że współczynniki regresji odpornej mogą się istotnie różnic od współczynników uzyskanych zwykłą metodą. Rożnice powodowane są obserwacjami odstającymi. Odpowiednie modele regresji rozpatrzone są ze względu na wydajność pracy w przeliczeniu na jednego zatrudnionego. (abstrakt oryginalny)
European countries can be characterized by indicators of general economic background, employment, innovation and research, science and technology. Values of these indicators are varying among European countries. The most used statistical tool for analyzing dependences is the regression analysis. The classical statistical approach - the least squares method (LS) may be highly unsatisfactory in the presence of outliers which can be supposed in analysis of European countries data. In such a case robust regression is acceptable and useful tool. The paper proves that the estimates of regression coefficients obtained by using a robust regression method can be significantly different from the ones obtained in the case of classical regression. The differences in results are significant namely in the cases where outliers and leverage points are identified. Some regression models suitable both from the point of view of goodness-of-fit test and satisfying t-tests and chi-square tests for individual parameters of regression models for Labour productivity per person employed are presented. (original abstract) - Accessibility
- The Main Library of the Cracow University of Economics
The Library of Warsaw School of Economics
The Library of University of Economics in Katowice
The Main Library of Poznań University of Economics and Business
The Main Library of the Wroclaw University of Economics - Full text
- Show
- Bibliography
-
- Antoch J., Ekblom H., Víšek J.A., Robust Estimation in Linear Model.XploRe Macros, http://www.quantlet.de/codes/rob/ROB.htlm 1999.
- Blatná D., "Robust model selection criteria", [in:] Applications of Mathematics and Statistics in Economy, Univerzita Mateje Bela, Banská Bystrica 2007, Občianske Združenie Financ, 2007, pp. 16-22.
- Blatná D., "Outliers in regression. Trutnov 30.08.2006 - 3.09.2006", [in:] AMSE 2006 [CD-ROM], KSTP VŠE, Praha 2006, pp. 1-6.
- Blatná D., "Robust regression", Prace Naukowe Akademii Ekonomicznej nr 1162, Application of Mathematics and Statistics in Economics, AE, Wrocław 2007, pp. 19-29.
- Blatná D., Robust Regression in Analysis of Internet Access in European Countries, Aplimat, Slovak University of Technology Bratislava 2008, pp. 1053-1061.
- Olive D., Applied Robust Statistics, preprint M-02-006, http://www.math.siu.edu/.
- Regression with SAS, http://www.ats.ucla.edu/stat/sas.
- Robust regression, http://en.wikipedia.org/wiki/Robust_regression.
- Rousseeuw P.J., Leroy A.M., Robust Regression and Outlier Detection, J.Wiley, New Jersey 2003.
- SAS 9.1.3 Help and Documentation.
- S-PLUS 6 Robust Library. User's Guide.
- The home of the S-PLUS statistical software package, http://www.insightful.com/.
- Wilcox R.R., Introduction to Robust Estimation and Hypothesis Testing, Academic Press, London 1999.
- Cited by
- ISSN
- 1899-3192
- Language
- eng
