Abstract:The detailed analysis of a regression model at various points of the conditional distribution, allowed by quantile regressions, can be imported into the least squares framework. This implies investiga...The detailed analysis of a regression model at various points of the conditional distribution, allowed by quantile regressions, can be imported into the least squares framework. This implies investigating the dependence among variables not only at the conditional mean but also in the tails, just as occurs in the quantile regression framework. Analogously to quantile regressions, expectiles allow to compute a regression model away from the conditional mean. M-quantiles merge together the M-estimators and the expectiles approach. The M-quantile estimator merges the weighting system curbing outliers of the M-estimators and the asymmetric weights defining the location of the expectiles. The purpose is to robustly compute the regression coefficients at different points of the conditional distribution of the dependent variable. The M-quantiles estimators are frequently used in small-area estimation. Examples analyzing real and artificial data sets point out the characteristics of the above estimators.Read More
Publication Year: 2018
Publication Date: 2018-08-20
Language: en
Type: other
Indexed In: ['crossref']
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Cited By Count: 1
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