Abstract
Abstract: An important assumption of linear regression model is that the variance of disturbances everywhere is equal (constant variance). However, unequal variance called heteroscedasticity does not cause biasness inestimates, but it leads to an efficientproblem and the standard errors of observations will be inaccurate. Under heteroscedasticity problem, the ordinary least squares estimates (OLS) are inefficient due to it gives same weights to all observations regardless of the fact that those with large residualscontain less information about regression model. The weighted least square (WLS) is a common method for remedy the heteroscedasticity problem. Unfortunately, in the presence of high leverage points (outlier in the predictor variables), the estimates of classical method such as OLS and WLS willbedamagedand aninefficient. In order to tackle the combined problem of heteroscedasticity and high leverage points, we suggested a new estimationmethod called robust quintile weighted least squares (RQWLS). Theresults of realdata example and simulation study shows that the suggested method has good performance compared with the existing methods.
Keywords
Heteroskedasticity
high leverage points
OLS
quintile regression
robust standard error
WLS