A most bias-robust linear estimate of the scale parameter of the exponential distribution
A remark on existence of statistical functionals
A robust test for variance
A simple robust estimator of correlations for Gaussian stationary random sequences
Adaptive estimation in linear regression model. II. Asymptotic normality
Adaptive maximum-likelihood-like estimation in linear models. I. Consistency
Adaptive maximum-likelihood-like estimation in linear models. II. Asymptotic normality
Adaptive procedures
Adaptive trimmed likelihood estimation in regression
In this paper we derive an asymptotic normality result for an adaptive trimmed likelihood estimator of regression starting from initial high breakdownpoint robust regression estimates. The approach leads to quickly and easily computed robust and efficient estimates for regression. A highlight of the method is that it tends automatically in one algorithm to expose the outliers and give least squares estimates with the outliers removed. The idea is to begin with a rapidly computed consistent robust...
An adaptive method of estimation and outlier detection in regression applicable for small to moderate sample sizes
In small to moderate sample sizes it is important to make use of all the data when there are no outliers, for reasons of efficiency. It is equally important to guard against the possibility that there may be single or multiple outliers which can have disastrous effects on normal theory least squares estimation and inference. The purpose of this paper is to describe and illustrate the use of an adaptive regression estimation algorithm which can be used to highlight outliers, either single or multiple...
An extension of Billingsley’s uniform boundedness theorem to higher-dimensional -processes
Are Robust Estimation Methods Useful in the Structural Errors-in-Variables Model?.
Asymptotic behaviour of the robust test in the Rieder's model of contamination
Asymptotic efficiency and robustness of -estimators
Asymptotics for robust MOSUM