Abstract
Analyses of linear models based on robust estimates of regression coefficients offers the user an attractive robust alternative to the classical least squares analysis in analyzing linear models. Much of the work done on robust analyses of linear models has concerned their asymptotic properties. To be of practical interest, though, the small sample properties of these analyses need to be ascertained. This article discusses studentization of these robust analyses and surveys past studies of it. With increasing speed of computation, resampling techniques have become feasible solutions to this studentizing problem. Some discussion of these techniques is also offered. To illustrate the discussion a Monte Carlo study of several experiments is included.
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McKean, J.W., Sheather, S.J. (1991). Small Sample Properties of Robust Analyses of Linear Models Based on R-Estimates: A Survey. In: Directions in Robust Statistics and Diagnostics. The IMA Volumes in Mathematics and its Applications, vol 34. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4444-8_1
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DOI: https://doi.org/10.1007/978-1-4612-4444-8_1
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