BazEkon - Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie

BazEkon home page

Meny główne

Autor
Dehnel Grażyna (Poznań University of Economics, Poland)
Tytuł
M-Estimators in Business Statistics
Źródło
Statistics in Transition, 2016, vol. 17, nr 4, s. 749-762, tab., wykr., bibliogr. s. 761-762
Słowa kluczowe
Estymatory, Estymacja, Statystyka gospodarcza, Modele regresji
Estimators, Estimation, Economic statistics, Regression models
Uwagi
summ., Materiały z konferencji Multivariate Statistical Analysis 2015, Łódź, The project is financed by the Polish National Science Centre, decision DEC- 2015/17/B/HS4/00905
Abstrakt
Recent years have seen a dynamic development in statistical methods for analysing data contaminated with outliers. One of the more important techniques that can deal with outlying observations is robust regression, which represents four decades of research. Until recently the implementation of robust regression methods, such as M-estimation or MM-estimation, was limited owing to their iterative nature. With advances in computing power and the growing availability of statistical packages, such as R and SAS, Stata, the applicability of robust regression methods has increased considerably.The aim of the study is to evaluate one of these methods, namely M-estimation, using data from a survey of small and medium-sized businesses. The comparison involves nine M-estimators, each based on a different weighting function. The results and conclusions are formulated on the basis of empirical data from the DG-1 business survey. (original abstract)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Pełny tekst
Pokaż
Bibliografia
Pokaż
  1. ALMA, Ö. G., (2011). Comparison of Robust Regression Methods in Linear Regression, [in:] Int. J. Contemp. Math. Sciences, Vol. 6, No. 9, pp. 409-421.
  2. BANAŚ, M., LIGAS, M., (2014). Empirical tests of performance of some M- estimators, Geodesy And Cartography, Vol. 63, No. 2, pp. 127-146.
  3. CHEN, C., (2007). Robust Regression and Outlier Detection with the ROBUSTREG Procedure, SUGI, http://www2.sas.com/proceedings/sugi27/ pp.265-27.pdf.
  4. CHEN, C., YIN, G., (2002). Computing the Efficiency and Tuning Constants for M-Estimation, Proceedings of the 2002 Joint Statistical Meetings, 478-482.
  5. COX, B. G., BINDER, A., CHINNAPPA, N. B., CHRISTIANSON, A., COLLEDGE, M. J., KOTT, P. S., (1995). Business Survey Methods, John Wiley and Sons.
  6. FAIR, R. C. (1974). On the robust estimation of econometric models, Ann. Econ. Social Measurement, 3, pp. 667-678.
  7. HAMPEL, F. R., RONCHETTI, E. M., ROUSSEEUW, P. J. STAHEL, W. A., (1986). Robust Statistics: The Approach Based on Influence Functions, Wiley, New York.
  8. HOLLAND, P., WELSCH, R., (1977). Robust Regression Using Interactively Reweighted Least-Squares, Commun. Statist. Theor. Meth., 6, 813-827.
  9. HUBER, P. H., (1964). Robust estimation of a location parameter, The Annals of Mathematical Statistics, 35, pp. 7-101.
  10. HUBER, P. H., (1981). Robust Statistics, New York: John Wiley and Sons.
  11. RIPLEY, B. D., (2004). Robust Statistics, M.Sc. in Applied Statistics MT2004, 1992-2004, https://www.stats.ox.ac.uk/pub/StatMeth/Robust.pdf.
  12. ROUSSEEUW, P. J., LEROY, A. M., (1987). Robust Regression and Outlier Detection. Wiley-Interscience, New York.
  13. SAS INSTITUTE INC., (2014). SAS/STAT® 13.2 User's Guide. The Robustreg Procedure Cary, NC: SAS Institute Inc.
  14. STROMBERG, A. J., (1993). Computation of high breakdown nonlinear regression parameters, [in:] Journal of the American Statistical Association, 88 (421).
  15. TRZPIOT, G., (2013). Wybrane statystyki odporne [Selected resistant statistics], [in:] Studia Ekonomiczne, No. 152, pp. 162-173, Uniwersytet Ekonomiczny w Katowicach.
  16. VENABLES, W. N., RIPLEY, B. D., (2002). Modern Applied Statistics with S- PLUS. Springer-Verlag.
  17. VERARDI, V., CROUX, C., (2009). Robust regression in Stata, [in:] The Stata Journal, 9, No. 3, pp. 439-453.
Cytowane przez
Pokaż
ISSN
1234-7655
Język
eng
Udostępnij na Facebooku Udostępnij na Twitterze Udostępnij na Google+ Udostępnij na Pinterest Udostępnij na LinkedIn Wyślij znajomemu