Please use this identifier to cite or link to this item:
|Title:||Assessing trimming methodologies for clustering linear regression data|
|Authors:||TORTI FRANCESCA; PERROTTA DOMENICO; RIANI MARCO; CERIOLI ANDREA|
|Citation:||ADVANCES IN DATA ANALYSIS AND CLASSIFICATION p. 1-31|
|Type:||Articles in periodicals and books|
|Abstract:||We assess the performance of state-of-the-art robust clustering tools for regression structures under a variety of different data configurations. We focus on two methodologies that use trimming and restrictions on group scatters as their main ingredients. We also give particular care to the data generation process through the development of a flexible simulation tool for mixtures of regressions, where the user can control the degree of overlap between the groups. Level of trimming and restriction factors are input parameters for which appropriate tuning is required. Since we find that incorrect specification of the second-level trimming in the Trimmed CLUSTering REGression model (TCLUST-REG) can deteriorate the performance of the method, we propose an improvement where the second-level trimming is not fixed in advance but is data dependent.We then compare our adaptive version of TCLUST-REG with the Trimmed Cluster Weighted Restricted Model (TCWRM) which provides a powerful extension of the robust clusterwise regression methodology. Our overall conclusion is that the two methods perform comparably, but with notable differences due to the inherent degree of modeling implied by them.|
|JRC Directorate:||Joint Research Centre Corporate Activities|
Files in This Item:
There are no files associated with this item.
Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.