The authors are to be commended for bringing the critical problem of cellwise outliers to the attention of a broader community and providing some important new estimation methods and related theory. High dimensional data analysis has become a critical area for research in statistical theory and practice and, in such situations, removing (or severely downweighting) an entire observation for a single cellwise outlier can eliminate most of the data
In robust statistics it is generally assumed that the majority of the observations is free of contam...
In robust statistics it is generally assumed that the majority of the observations is free of contam...
In multiple regression analysis, a response variable is predicted based on a set of p predictor vari...
El autor realiza dos observaciones acerca del artículo "Robust estimation of multivariate location a...
El autor realiza dos observaciones acerca del artículo "Robust estimation of multivariate location a...
Real data may contain both cellwise outliers and casewise outliers. There is a vast literature on ro...
Cellwise outliers are likely to occur together with casewise outliers in datasets of relatively larg...
A multivariate dataset consists of n observations in p dimensions, and is often stored in an n by p ...
A multivariate dataset consists of n observations in p dimensions, and is often stored in an n by p ...
Standard statistical techniques such as least squares regression are very accurate if the underlying...
The sample mean can have poor efficiency relative to various alternative estimators under arbitraril...
We investigate the performance of robust estimates of multi- variate location under non-standard dat...
Cellwise outliers are widespread in data and traditional robust methods may fail when applied to dat...
We investigate the performance of robust estimates of multivariate location under nonstandard data c...
In order to describe or generate so-called outliers in univariate statistical data, contamination mo...
In robust statistics it is generally assumed that the majority of the observations is free of contam...
In robust statistics it is generally assumed that the majority of the observations is free of contam...
In multiple regression analysis, a response variable is predicted based on a set of p predictor vari...
El autor realiza dos observaciones acerca del artículo "Robust estimation of multivariate location a...
El autor realiza dos observaciones acerca del artículo "Robust estimation of multivariate location a...
Real data may contain both cellwise outliers and casewise outliers. There is a vast literature on ro...
Cellwise outliers are likely to occur together with casewise outliers in datasets of relatively larg...
A multivariate dataset consists of n observations in p dimensions, and is often stored in an n by p ...
A multivariate dataset consists of n observations in p dimensions, and is often stored in an n by p ...
Standard statistical techniques such as least squares regression are very accurate if the underlying...
The sample mean can have poor efficiency relative to various alternative estimators under arbitraril...
We investigate the performance of robust estimates of multi- variate location under non-standard dat...
Cellwise outliers are widespread in data and traditional robust methods may fail when applied to dat...
We investigate the performance of robust estimates of multivariate location under nonstandard data c...
In order to describe or generate so-called outliers in univariate statistical data, contamination mo...
In robust statistics it is generally assumed that the majority of the observations is free of contam...
In robust statistics it is generally assumed that the majority of the observations is free of contam...
In multiple regression analysis, a response variable is predicted based on a set of p predictor vari...