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Please use this identifier to cite or link to this item: 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
Citation: Statistical methods in medical research,p.1-24
Status: Early View
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: Corresponding author: Liesbeth Bruckers, Universiteit Hasselt, Martelarenlaan 42, Hasselt 3500, Belgium. Email: liesbeth.bruckers@uhasselt.be
URI: http://hdl.handle.net/1942/20870
DOI: 10.1177/0962280216634112
ISSN: 0962-2802
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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