Robust Regression in Monthly Business Survey

Dehnel, Grażyna

Author

Dehnel Grażyna (Poznań University of Economics, Poland)

Title

Robust Regression in Monthly Business Survey

Source

Statistics in Transition, 2015, vol. 16, nr 1, s. 137-152, rys., tab., bibliogr. s. 151-152

Keyword

Odporne metody statystyczne, Statystyka, Obserwacje nietypowe
Robust statistical methods, Statistics, Outliers

Note

summ.
Materiały z konferencji Multivariate Statistical Analysis 2014, Łódź.

Abstract

There are many sample surveys of populations that contain outliers (extreme values). This is especially true in business, agricultural, household and medicine surveys. Outliers can have a large distorting influence on classical statistical methods that are optimal under the assumption of normality or linearity. As a result, the presence of extreme observations may adversely affect estimation, especially when it is carried out at a low level of aggregation. To deal with this problem, several alternative techniques of estimation, less sensitive to outliers, have been proposed in the statistical literature. In this paper we attempt to apply and assess some robust regression methods (LTS, M-estimation, S-estimation, MM-estimation) in the business survey conducted within the framework of official statistics. (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

Full text

Bibliography

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.
CHEN, C., (2007). Robust Regression and Outlier Detection with the ROBUSTREG Procedure, SUGI, http://www2.sas.com/proceedings/sugi27/pp.265-27.pdf.
COX, B. G., BINDER, A., CHINNAPPA, N. B., CHRISTIANSON, A., COLLEDGE, M. J., KOTT, P. S., (1995). Business Survey Methods, John Wiley and Sons.
GROSS, W. F., BODE, G., TAYLOR, J. M., LLOYD-SMITH, C. W., (1986). Some finite population estimators which reduce the contribution of outliers, [in:] Proceedings of the Pacific Statistical Conference, 20-24 May 1985, Auckland, New Zealand.
HUBER, P. H., (1964). Robust estimation of a location parameter, The Annals of Mathematical Statistics, 35, pp.7-101.
HUBER, P. H., (1981). Robust Statistics, New York: John Wiley and Sons.
ROUSSEEUW, P. J., (1984). Least Median of Squares Regression, [in:] Journal of the American Statistical Association, 79, pp. 871- 880.
ROUSSEEUW, P. J., YOHAI, V., (1984). Robust regression by means of S-estimators, [in:] W. H. J. Franke and D. Martin (Editors.), Robust and Nonlinear Time Series Analysis, Springer-Verlag, New-York, pp. 256-272.
ROUSSEEUW, P. J., LEROY, A. M., (1987). Robust Regression and Outlier Detection. Wiley-Interscience, New York.
ROUSSEEUW, P. J., DRIESSEN, K., (1998). Computing LTS regression for large data sets, Technical Report, University of Antwerp.
STROMBERG, A. J., (1993). Computation of high breakdown nonlinear regression parameters, [in:] Journal of the American Statistical Association, 88 (421).
VERARDI, V., CROUX, C., (2009). Robust regression in Stata, [in:] The Stata Journal, 9, Number 3, pp. 439-453.
YOHAI,V.J., (1987). High breakdown-point and high efficiency robust estimates for regression, The Annals of Statistics, 15, pp. 642-656.

Cited by

ISSN

1234-7655

Language

eng

BazEkon - The Main Library of the Cracow University of Economics

Main menu