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Antonio D’Ambrosio, W. Heiser (2018)
A distribution-free soft-clustering method for preference rankingsBehaviormetrika
Yuki Yamagishi, Kensuke Tanioka, Hiroshi Yadohisa (2019)
Constrained nonmetric principal component analysisBehaviormetrika
K. Fukumoto, Andreas Beger, W. Moore (2019)
Bayesian modeling for overdispersed event-count time seriesBehaviormetrika, 46
Takahiro Onoshima, K. Shiina, T. Ueda, Saori Kubo (2019)
Decline of Pearson’s r with categorization of variables: a large-scale simulationBehaviormetrika, 46
Shenghai Dai, Xiaolin Wang, Dubravka Svetina (2019)
The application of minimum discrepancy estimation in implementation of cognitive diagnostic modelsBehaviormetrika, 46
J. Durieux, T. Wilderjans (2019)
Partitioning subjects based on high-dimensional fMRI data: comparison of several clustering methods and studying the influence of ICA data reduction in big dataBehaviormetrika, 46
Edward Haertel (1989)
Using restricted latent class models to map the skill structure of achievement itemsJournal of Educational Measurement, 26
Hanneke Hoef, M. Warrens (2018)
Understanding information theoretic measures for comparing clusteringsBehaviormetrika
Yasuo Miyazaki, Youngyun Chungbaek, K. Shropshire, D. Hedeker (2019)
Consequences of ignoring nested data structure on item parameters in Rasch/1P-IRT modelBehaviormetrika, 46
M. Vichi, Donatella Vicari, H. Kiers (2015)
Clustering and dimension reduction for mixed variablesBehaviormetrika
B. Junker, K. Sijtsma (2001)
Cognitive Assessment Models with Few Assumptions, and Connections with Nonparametric Item Response TheoryApplied Psychological Measurement, 25
Behaviormetrika https://doi.org/10.1007/s41237-019-00096-2 EDITORIAL Maomi Ueno © The Behaviormetric Society 2019 Welcome to the vol. 46, no. 2, 2019 of Behaviormetrika. In this issue, we have the following eight original papers, one short note and one invited paper. This issue includes a special feature: “Dimension reduction and cluster analysis” (Vichi et al. 2019; Durieux and Wilderjans 2019; Yamagish et al. 2019; D’Ambrosio and Heiser 2019; van der Hoef and Warrens 2019) which was edited by Michel van de Velden, Alfonso Iodice D’Enza and Michio Yamamoto. This special feature specifically addresses state-of-the-art clustering methods. The regular papers of this issue include the following four original papers and one short note. • The original paper ‘‘Bayesian model checking in cognitive diagnostic models” by Nan Wang and Russell Alomond (2019) proposes to use prior predictive posterior simulation method and posterior predictive method to investigate the person fit of DINA model (the deterministic inputs, noisy “and” gate) (Haertel 1989; Junker and Sijtsma 2001). The rationale of the Bayesian model checking method is to compare the discrepancy measure that calculates with the observed data to a distribution obtained by applying it to multiple simulated data sets. The result of this study might help researchers to choose the appropriate
Behaviormetrika – Springer Journals
Published: Sep 24, 2019
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