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Journal of Classification
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22-05-2019

Editorial: Journal of Classification Vol 36-1

Author: Douglas L. Steinley

Published in: Journal of Classification | Issue 1/2019

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Excerpt

The first issue of 2019 has eight papers that cover a wide range of topics, from mixture modeling to traditional clustering and classification methods. The first paper is from Flynt and Dean on growth mixture modeling. Specifically, they look at the prospect of incorporating variable selection in the growth mixture model—which is a somewhat radical idea as it is usually assumed that all the measurement occasions are required to accurately estimate the growth trajectories; however, the authors show the selecting specific measurement occasions that separate the clusters well can lead to improved recovery and reduced error. I imagine that this paper will serve as a springboard for incorporating other variable selection methods, such as those described in Steinley and Brusco (2008), into growth mixture modeling. …
next article Growth Mixture Modeling with Measurement Selection

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Metadata
Title
Editorial: Journal of Classification Vol 36-1
Author
Douglas L. Steinley
Publication date
22-05-2019
Publisher
Springer US
Published in
Journal of Classification / Issue 1/2019
Print ISSN: 0176-4268
Electronic ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-019-09334-4

Other articles of this Issue 1/2019

Multiclass Classification Based on Multi-criteria Decision-making

A Mixture of Coalesced Generalized Hyperbolic Distributions

Growth Mixture Modeling with Measurement Selection

Comparison of Similarity Measures for Categorical Data in Hierarchical Clustering

A New Method for Classifying Random Variables Based on Support Vector Machine

Robustification of Gaussian Bayes Classifier by the Minimum β-Divergence Method

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