Add to ORKGIn this paper we examine some of the relationships between two important optimization problems that arise in statistics: robust estimation of multi-variate location and shape parameters and maximum likelihood assignment of multivariate data to clusters. We offer a synthesis and generalization o
This thesis focuses on developing a few robust learning algorithms, which aim to overcome the major ...
Standard procedures of multivariate statistics and data mining for the analysis of multivariate data...
We propose a new data-driven technique for constructing uncertainty sets for robust optimization pro...
In this paper we examine some of the relationships between two important optimization problems that ...
We examine relationships between the problem of robust estimation of multivariate location and shape...
Clustering remains a vibrant area of research in statistics. Although there are many books on this t...
In this paper, we describe an overall strategy for robust estimation of multivariate location and sh...
Abstract—Clustering methods need to be robust if they are to be useful in practice. In this paper, w...
International audienceSome recent contributions to robust inference are presented. Firstly, the clas...
Support vector based spherical clustering is described as an optimization problem posed in the input...
Monitoring the properties of single sample robust analyses of multivariate data as a function of bre...
The forward search provides a series of robust parameter estimates based on increasing numbers of ob...
A new method for performing robust clustering is proposed. The method is designed with the aim of fi...
The Optimally Tuned Robust Improper Maximum Likelihood Estima- tor (OTRIMLE) for robust model-based ...
This presentation studies robust inference for regression models where data are clustered, with corr...
This thesis focuses on developing a few robust learning algorithms, which aim to overcome the major ...
Standard procedures of multivariate statistics and data mining for the analysis of multivariate data...
We propose a new data-driven technique for constructing uncertainty sets for robust optimization pro...
In this paper we examine some of the relationships between two important optimization problems that ...
We examine relationships between the problem of robust estimation of multivariate location and shape...
Clustering remains a vibrant area of research in statistics. Although there are many books on this t...
In this paper, we describe an overall strategy for robust estimation of multivariate location and sh...
Abstract—Clustering methods need to be robust if they are to be useful in practice. In this paper, w...
International audienceSome recent contributions to robust inference are presented. Firstly, the clas...
Support vector based spherical clustering is described as an optimization problem posed in the input...
Monitoring the properties of single sample robust analyses of multivariate data as a function of bre...
The forward search provides a series of robust parameter estimates based on increasing numbers of ob...
A new method for performing robust clustering is proposed. The method is designed with the aim of fi...
The Optimally Tuned Robust Improper Maximum Likelihood Estima- tor (OTRIMLE) for robust model-based ...
This presentation studies robust inference for regression models where data are clustered, with corr...
This thesis focuses on developing a few robust learning algorithms, which aim to overcome the major ...
Standard procedures of multivariate statistics and data mining for the analysis of multivariate data...
We propose a new data-driven technique for constructing uncertainty sets for robust optimization pro...