Approximate Tau-Estimates For Linear Regression Based On Subsampling Of Elemental

ADROVER, JORGE GABRIEL; BIANCO, ANA MARIA; YOHAI, VICTOR JAIME

STATISTICAL IN GENETICS AND ENVIRONMENTAL SCIENCES

Birkhauser

Lugar: Basilea, Suiza; Año: 2001; p. 173 - 183

In this paper we show that approximate tau-estimates for the linear model,computed by the algorithm based on subsampling of elemental subsets, are consistent and with high probability have the same breakdown point that the exact tau-estimate. Then, if these estimates are used as initial values, the reweighted least squares algorithm yields a local minimum of the tau-scale having the same asymptotic distribution and, with high probability, the same breakdown point that the global minimum. tau-estimates for the linear model,computed by the algorithm based on subsampling of elemental subsets, are consistent and with high probability have the same breakdown point that the exact tau-estimate. Then, if these estimates are used as initial values, the reweighted least squares algorithm yields a local minimum of the tau-scale having the same asymptotic distribution and, with high probability, the same breakdown point that the global minimum. tau-estimates for the linear model,computed by the algorithm based on subsampling of elemental subsets, are consistent and with high probability have the same breakdown point that the exact tau-estimate. Then, if these estimates are used as initial values, the reweighted least squares algorithm yields a local minimum of the tau-scale having the same asymptotic distribution and, with high probability, the same breakdown point that the global minimum.