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M-Estimators in Business Statistics

Author

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  • Dehnel Grażyna

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

Abstract

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.

Suggested Citation

  • Dehnel Grażyna, 2016. "M-Estimators in Business Statistics," Statistics in Transition New Series, Statistics Poland, vol. 17(4), pages 749-762, December.
    • Handle: RePEc:vrs:stintr:v:17:y:2016:i:4:p:749-762:n:4
    DOI: 10.21307/stattrans-2016-050
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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.
    2. Ray C. Fair, 1974. "On the Robust Estimation of Econometric Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 667-677, National Bureau of Economic Research, Inc.
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