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http://dx.doi.org/10.5351/KJAS.2006.19.1.097

On Confidence Intervals of Robust Regression Estimators  

Lee Dong-Hee (Institute of Statistics, Korea University)
Park You-Sung (Dept. of Statistics, Korea University)
Kim Kee-Whan (Dept. of Informational Statistics, Korea University)
Publication Information
The Korean Journal of Applied Statistics / v.19, no.1, 2006 , pp. 97-110 More about this Journal
Abstract
Since it is well-established that even high quality data tend to contain outliers, one would expect fat? greater reliance on robust regression techniques than is actually observed. But most of all robust regression estimators suffers from the computational difficulties and the lower efficiency than the least squares under the normal error model. The weighted self-tuning estimator (WSTE) recently suggested by Lee (2004) has no more computational difficulty and it has the asymptotic normality and the high break-down point simultaneously. Although it has better properties than the other robust estimators, WSTE does not have full efficiency under the normal error model through the weighted least squares which is widely used. This paper introduces a new approach as called the reweighted WSTE (RWSTE), whose scale estimator is adaptively estimated by the self-tuning constant. A Monte Carlo study shows that new approach has better behavior than the general weighted least squares method under the normal model and the large data.
Keywords
confidence interval; coverage probability; high breakdown point; joint confidence region; outliers; robust regression estimation;
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