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ASYMPTOTICS FOR REDESCENDING M-ESTIMATORS IN LINEAR MODELS WITH INCREASING DIMENSION

机译:随着尺寸的增加,用于在线性模型中重新介绍M估计的渐近性

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This paper deals with the asymptotic statistical properties of a class of redescending M-estimators in linear models with increasing dimension. This class is large enough to include popular high breakdown point estimators such as S-estimators and MM-estimators, which were not covered by existing results in the literature. We prove consistency assuming only that p/n -> 0 and asymptotic normality essentially if p(3)/n -> 0, where p is the number of covariates and n is the sample size.
机译:本文涉及随着尺寸增加的线性模型中一类重建M估计的渐近统计特性。 该类足够大,以包括流行的高击穿点估计,例如S估算器和MM估算器,这些估算器在文献中未被现有结果覆盖。 我们证明了一致性假设仅P / N - > 0和渐近常规,基本上是P(3)/ n - > 0,其中P是协变量的数量,n是样本大小。

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