Quantile regression
1 article · search the full text for this term
-
Comparison between Generalized Least Square Method and Quantile Regression in Dealing with Heteroscedastic Data
Abstract: Heteroscedastic data arise from a population whose sub-populations have different variabilities from one another. In presence of heteroscedasticity, the OLS estimators no longer remain the BLUEs, giving rise to wider acceptance regions and confidence intervals for the model parameters. The generalized least square technique, specifically weighted least square method is the conventional way of eliminating heteroscedasticity. However, quantile regression can also be used in dealing with this kind of data. …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 3, 2018 · pp. 12–16 Read article