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- W2925133080 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. This paper aims at comparing the efficiencies of these two methods. Keywords: Heteroscedasticity, generalized least square, quantile regression, efficiency Cite this Article Samhita Pal. Comparison between Generalized Least Square Method and Quantile Regression in Dealing with Heteroscedastic Data. Research & Reviews: Journal of Statistics . 2018; 7(3): 12–16p." @default.
- W2925133080 created "2019-04-01" @default.
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- W2925133080 date "2018-10-31" @default.
- W2925133080 modified "2023-09-27" @default.
- W2925133080 title "Comparison between Generalized Least Square Method and Quantile Regression in Dealing with Heteroscedastic Data" @default.
- W2925133080 doi "https://doi.org/10.37591/rrjost.v7i3.1325" @default.
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