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On the Utility of Robust and Resampling Procedures 1
Author(s) -
Dietz Thomas,
Kalof Linda,
Frey R. Scott
Publication year - 1991
Publication title -
rural sociology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.083
H-Index - 65
eISSN - 1549-0831
pISSN - 0036-0112
DOI - 10.1111/j.1549-0831.1991.tb00443.x
Subject(s) - ordinary least squares , outlier , robust regression , econometrics , bootstrapping (finance) , estimator , resampling , regression , linear regression , standard error , statistics , residual , distribution (mathematics) , macro , computer science , mathematics , algorithm , programming language , mathematical analysis
Kassab (1990) makes an important methodological contribution by urging the use of robust regression methods in the study of community economic impacts and by indicating the utility of the bootstrap in assessing standard errors in robust regression. By introducing the notion of a contaminating distribution, we reconcile differences between her claim that ordinary least squares (OLS) regression is biased when outliers are present and standard linear model theory that does not make assumptions about the shape of the residual distribution in proving OLS an unbiased estimator. The contaminating distribution provides a framework for rural sociologists to link their statistical assumptions to a substantive understanding of the phenomena being studied. We suggest an alternative regression estimation strategy that may be more robust than the technique she uses. We also discuss an approach to bootstrapping that is more appropriate for macro‐level social indicator data than the one she describes. An appendix discusses the software available for implementing these methods.