A Fast Algorithm for S-Regression Estimates

MATÍAS SALIBIAN-BARRERA; VÍCTOR J. YOHAI

JOURNAL OF COMPUTACIONAL & GRAPHICAL STATISTICS

American Statistical Association

Lugar: Alexandria, Virginia, USA; Año: 2006 vol. 15 p. 414 - 427

1061-8600

Equivariant high-breakdown point regression estimates are computationally expensive, and the corresponding algorithms become unfeasible for moderately large number of regressors. One important advance to improve the computational speed of one such estimator is the fast-LTS algorithm. This article proposes an analogous algorithm for computing S-estimates. The new algorithm, that we call “fast-S”, is also based on a “local improvement” step of the resampling initial candidates. This allows for a substantial reduction of the number of candidates required to obtain a good approximation to the optimal solution. We performed a simulation study which shows that S-estimators computed with the fast-S algorithm compare favorably to the LTS-estimators computed with the fast-LTS algorithm.