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Robust Regression in Monthly Business Survey

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  • Dehnel Grażyna

    (Poznan University of Economics, Department of Statistics, ; Poznan, ; Poland)

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.

Suggested Citation

  • Dehnel Grażyna, 2015. "Robust Regression in Monthly Business Survey," Statistics in Transition New Series, Statistics Poland, vol. 16(1), pages 137-152, March.
    • Handle: RePEc:vrs:stintr:v:16:y:2015:i:1:p:137-152:n:1
    DOI: 10.21307/stattrans-2015-008
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    References listed on IDEAS

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    1. Vincenzo Verardi & Christophe Croux, 2009. "Robust regression in Stata," Stata Journal, StataCorp LP, vol. 9(3), pages 439-453, September.
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