Add to ORKGRecent work on robust estimation has led to many procedures, which are easy to formulate and straightforward to program but difficult to study analytically. In such circumstances experimental sampling is quite attractive, but the variety and complexity of both estimators and sampling situations make effective Monte Carlo techniques essential. This discussion examines problems, techniques, and results and draws on examples in studies of robust location and robust regression.
We study the problem of performing statistical inference based on robust estimates when the distrib...
AbstractA robust estimator of the regression function is proposed combining kernel methods as introd...
This paper elaborates on the deleterious effects of outliers and corruption of dataset on estimation...
A preeminent expert in the field explores new and exciting methodologies in the ever-growing field o...
Econometrics often deals with data under, from the statistical point of view, non-standard condition...
Abstract: Robust methods are little applied (although much studied by statisticians). We monitor ver...
Methods of robust statistics are successfully used in the situations when certain sample values diff...
The sample mean can have poor efficiency relative to various alternative estimators under arbitraril...
Includes bibliography.This study initially set out to consider the possibility of constructing an ad...
[Δε διατίθεται περίληψη / no abstract available][Δε διατίθεται περίληψη / no abstract available
International audienceWe compare 43 location estimators as regards their robustness through a Monte ...
It is argued that a main aim of statistics is to produce statistical procedures which in this articl...
High breakdown point, bounded influence and high efficiency at the Gaussian model are desired proper...
This text presents methods that are robust to the assumption of a multivariate normal distribution o...
In statistical theory and practice, a certain distribution is usually assumed and then optimal solut...
We study the problem of performing statistical inference based on robust estimates when the distrib...
AbstractA robust estimator of the regression function is proposed combining kernel methods as introd...
This paper elaborates on the deleterious effects of outliers and corruption of dataset on estimation...
A preeminent expert in the field explores new and exciting methodologies in the ever-growing field o...
Econometrics often deals with data under, from the statistical point of view, non-standard condition...
Abstract: Robust methods are little applied (although much studied by statisticians). We monitor ver...
Methods of robust statistics are successfully used in the situations when certain sample values diff...
The sample mean can have poor efficiency relative to various alternative estimators under arbitraril...
Includes bibliography.This study initially set out to consider the possibility of constructing an ad...
[Δε διατίθεται περίληψη / no abstract available][Δε διατίθεται περίληψη / no abstract available
International audienceWe compare 43 location estimators as regards their robustness through a Monte ...
It is argued that a main aim of statistics is to produce statistical procedures which in this articl...
High breakdown point, bounded influence and high efficiency at the Gaussian model are desired proper...
This text presents methods that are robust to the assumption of a multivariate normal distribution o...
In statistical theory and practice, a certain distribution is usually assumed and then optimal solut...
We study the problem of performing statistical inference based on robust estimates when the distrib...
AbstractA robust estimator of the regression function is proposed combining kernel methods as introd...
This paper elaborates on the deleterious effects of outliers and corruption of dataset on estimation...