Gini Regression Analysis Export

@article{citeulike:6064700, abstract = {The method of least squares ranks as one of the most commonly used methods for estimating the relation between a set of variables on the conditional expected value of another variable. Ordinary Least Squares (OLS) relies on several assumptions, which when violated may not yield robust estimates. In this paper we pose alternative ways to view this model. In particular we study Gini regression which is equivalent to OLS when the assumptions hold, and is more robust when the assumptions fail. Gini regression can also be used to test the validity of the assumptions.}, author = {Olkin, Ingram and Yitzhaki, Shlomo}, citeulike-article-id = {6064700}, citeulike-linkout-0 = {http://dx.doi.org/10.2307/1403649}, citeulike-linkout-1 = {http://www.jstor.org/stable/1403649}, doi = {10.2307/1403649}, issn = {03067734}, journal = {International Statistical Review / Revue Internationale de Statistique}, keywords = {gini, regression, robust}, number = {2}, pages = {185--196}, posted-at = {2009-11-03 21:21:44}, priority = {3}, publisher = {International Statistical Institute (ISI)}, title = {Gini Regression Analysis}, url = {http://dx.doi.org/10.2307/1403649}, volume = {60}, year = {1992} }

TY - JOUR ID - citeulike:6064700 L3 - citeulike-article-id:6064700 N2 - The method of least squares ranks as one of the most commonly used methods for estimating the relation between a set of variables on the conditional expected value of another variable. Ordinary Least Squares (OLS) relies on several assumptions, which when violated may not yield robust estimates. In this paper we pose alternative ways to view this model. In particular we study Gini regression which is equivalent to OLS when the assumptions hold, and is more robust when the assumptions fail. Gini regression can also be used to test the validity of the assumptions. IS - 2 JF - International Statistical Review / Revue Internationale de Statistique SN - 03067734 EP - 196 TI - Gini Regression Analysis PB - International Statistical Institute (ISI) VL - 60 SP - 185 KW - gini KW - regression KW - robust AU - Olkin, Ingram AU - Yitzhaki, Shlomo PY - 1992/// UR - http://dx.doi.org/10.2307/1403649 ER -