Mammen, Enno Asymptotics with increasing dimension for robust regression with applications to the bootstrap. (English) Zbl 0674.62017 Ann. Stat. 17, No. 1, 382-400 (1989). Summary: A stochastic expansion for M-estimates in linear models with many parameters is derived under the weak condition \(\kappa\) \(n^{1/3}(\log n)^{2/3}\to 0\), where n is the sample size and \(\kappa\) the maximal diagonal element of the hat matrix. The expansion is used to study the asymptotic distribution of linear contrasts and the consistency of the bootstrap. In particular, it turns out that bootstrap works in cases where the usual asymptotic approach fails. Cited in 48 Documents MSC: 62E20 Asymptotic distribution theory in statistics 62F35 Robustness and adaptive procedures (parametric inference) 62J05 Linear regression; mixed models Keywords:increasing dimension; robust regression; asymptotic normality; stochastic expansion; M-estimates; linear models; linear contrasts; consistency; bootstrap × Cite Format Result Cite Review PDF Full Text: DOI