Add to ORKGThis paper presents a method for detecting multivariate outliers which might be distorting theı estimation of a transformation to normality. A robust estimator of the transformation parameter is also proposed
In investigations on the behaviour of robust estimators, typically their consistency and their asymp...
Outlier identification is important in many applications of multivariate analysis. Either because th...
Statistics deals with gaining information from data. In practice, data often contain some randomness...
This paper presents a method for detecting multivariate outliers which might be distorting theı esti...
The assumption of normality provides the customary powerful and convenient way of analyzing linear r...
The classical multivariate theory has been largely based on the multivariate normal distribution (MV...
When applying a statistical method in practice it often occurs that some observations deviate from t...
This text presents methods that are robust to the assumption of a multivariate normal distribution o...
Classic methods in multivariate analysis require the estimat.ion of mean vectors and covariance matr...
We propose a new procedure for computing an approximation to regression estimates based on the minim...
In this article, we present a simple multivariate outlier-detection procedure and a robust estimator...
Outliers can have a large influence on the model fitted to data. The models we consider are the tran...
Beran (2003) defined statistics as the study of algorithms for data analysis. In many situations se...
A collection of robust Mahalanobis distances for multivariate outlier detection is proposed, based o...
In today’s society, statistical techniques are being used widely in education, medicine, social scie...
In investigations on the behaviour of robust estimators, typically their consistency and their asymp...
Outlier identification is important in many applications of multivariate analysis. Either because th...
Statistics deals with gaining information from data. In practice, data often contain some randomness...
This paper presents a method for detecting multivariate outliers which might be distorting theı esti...
The assumption of normality provides the customary powerful and convenient way of analyzing linear r...
The classical multivariate theory has been largely based on the multivariate normal distribution (MV...
When applying a statistical method in practice it often occurs that some observations deviate from t...
This text presents methods that are robust to the assumption of a multivariate normal distribution o...
Classic methods in multivariate analysis require the estimat.ion of mean vectors and covariance matr...
We propose a new procedure for computing an approximation to regression estimates based on the minim...
In this article, we present a simple multivariate outlier-detection procedure and a robust estimator...
Outliers can have a large influence on the model fitted to data. The models we consider are the tran...
Beran (2003) defined statistics as the study of algorithms for data analysis. In many situations se...
A collection of robust Mahalanobis distances for multivariate outlier detection is proposed, based o...
In today’s society, statistical techniques are being used widely in education, medicine, social scie...
In investigations on the behaviour of robust estimators, typically their consistency and their asymp...
Outlier identification is important in many applications of multivariate analysis. Either because th...
Statistics deals with gaining information from data. In practice, data often contain some randomness...