Title: Assessing trimming methodologies for clustering linear regression data
Authors: TORTI FRANCESCAPERROTTA DOMENICORIANI MARCOCERIOLI ANDREA
Citation: ADVANCES IN DATA ANALYSIS AND CLASSIFICATION p. 1-31
Publisher: SPRINGER HEIDELBERG
Publication Year: 2019
JRC N°: JRC104609
ISSN: 1862-5347 (online)
URI: https://link.springer.com/article/10.1007%2Fs11634-018-0331-4
http://publications.jrc.ec.europa.eu/repository/handle/JRC104609
DOI: 10.1007/s11634-018-0331-4
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.
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