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Integrating a robust option into a multiple regression computing environment
William Dumouchel,Fanny O'Brien +1 more
- 03 Jan 1992
- pp 41-48
175
About: The article was published on 03 Jan 1992. and is currently open access. The article focuses on the topics: Robust regression & Linear regression.
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References
•Book
Understanding robust and exploratory data analysis
David C. Hoaglin,Frederick Mosteller,John W. Tukey +2 more
- 01 Jan 1983
TL;DR: Estimators of Location: An Outline of the Theory (C. Goodall).
2.1K
A Note on Computing Robust Regression Estimates via Iteratively Reweighted Least Squares
TL;DR: The 1985 SAS User's Guide: Statistics provides a method for computing robust regression estimates using iterative reweighted least squares and the nonlinear regression procedure NLIN, and it is shown that the estimates are asymptotically correct, although the resulting standard errrors are not.
Confidence Intervals for Bisquare Regression Estimates
TL;DR: In this paper, a Monte Carlo study of robust regression confidence-interval estimation in the model y = a + bx + e was carried out on samples of 11 and 21 with five design matrices.
82
The structure, design principles, and strategies of Mulreg
TL;DR: Design features of Mulreg are the object orientation of its internal data structures and of its user interface, its integration of graphics into every stage of the modeling process, the guidance provided by the menu layout, and the choice of statistical algorithms, which provide a consistent data analysis strategy to attack the great diversity of regression-type problems.
9
Two Robust Alternatives to Least-Squares Regression
Richard W. Hill,Paul W. Holland +1 more
TL;DR: In this article, Monte Carlo results on the performance of two robust alternatives to least-squares regression estimation (least absolute residuals and the one-step sine estimator) are given.