You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Robust, Smoothly Heterogeneous Variance Regression
Michael Cohen, Siddhartha R. Dalal and John W. Tukey
Journal of the Royal Statistical Society. Series C (Applied Statistics)
Vol. 42, No. 2 (1993), pp. 339-353
Stable URL: http://www.jstor.org/stable/2986237
Page Count: 15
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
Under the simple linear regression model, we consider two violations of the standard assumptions, namely heterogeneous variances and long-tailed error distributions, in an integrated manner. A new method for estimation is proposed which assumes only that the heterogeneity is a locally smooth function of the regressor variable, except for outliers. The procedure is based on smoothing the non-outlying residuals from a robust regression to provide weights for a weighted regression. Monte Carlo results, some theory and a real data example are given. It is shown that the method is substantially more efficient than the usual robust regression methods in the presence of heterogeneity and only slightly worse when the variances are exactly equal.
Journal of the Royal Statistical Society. Series C (Applied Statistics) © 1993 Royal Statistical Society