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Robust regression in monthly business survey

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  • Grażyna Dehnel

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

  • Grażyna Dehnel, 2015. "Robust regression in monthly business survey," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(1), pages 137-152, May.
    • Handle: RePEc:csb:stintr:v:16:y:2015:i:1:p:137-152
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    File URL: http://index.stat.gov.pl/repec/files/csb/stintr/csb_stintr_v16_2015_i1_n9.pdf
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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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    Cited by:

    1. Dehnel Grażyna & Walesiak Marek, 2019. "A Comparative Analysis Of Economic Efficiency Of Medium-Sized Manufacturing Enterprises In Districts Of Wielkopolska Province Using The Hybrid Approach With Metric And Interval-Valued Data," Statistics in Transition New Series, Polish Statistical Association, vol. 20(2), pages 49-67, June.
    2. Grażyna Dehnel & Marek Walesiak, 2019. "A Comparative Analysis Of Economic Efficiency Of Medium-Sized Manufacturing Enterprises In Districts Of Wielkopolska Province Using The Hybrid Approach With Metric And Interval-Valued Data," Statistics in Transition New Series, Polish Statistical Association, vol. 20(2), pages 49-68, June.

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