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http://hdl.handle.net/1942/20870
Title: | Detecting influential observations in a model-based cluster analysis | Authors: | BRUCKERS, Liesbeth MOLENBERGHS, Geert VERBEKE, Geert GEYS, Helena |
Issue Date: | 2016 | Source: | Statistical methods in medical research, 27 (2), p.521-540 | Abstract: | Finite mixture models have been used to model population heterogeneity and to relax distributional assumptions. These models are also convenient tools for clustering and classification of complex data such as, for example, repeated-measurements data. The performance of model-based clustering algorithms is sensitive to influential and outlying observations. Methods for identifying outliers in a finite mixture model have been described in the literature. Approaches to identify influential observations are less common. In this paper, we apply local-influence diagnostics to a finite mixture model with known number of components. The methodology is illustrated on real-life data. | Notes: | Liesbeth Bruckers, Universiteit Hasselt, Martelarenlaan 42, Hasselt 3500, Belgium. Email: liesbeth.bruckers@uhasselt.be | Keywords: | local influence; finite mixture model; model-based clustering | Document URI: | http://hdl.handle.net/1942/20870 | ISSN: | 0962-2802 | e-ISSN: | 1477-0334 | DOI: | 10.1177/0962280216634112 | ISI #: | 000424710500014 | Rights: | © The Author(s) 2016 | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2019 vabb 2018 |
Appears in Collections: | Research publications |
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479.pdf | Peer-reviewed author version | 868.63 kB | Adobe PDF | View/Open |
bruckers2016.pdf Restricted Access | Published version | 571.47 kB | Adobe PDF | View/Open Request a copy |
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