Model selection for mixture-based clustering for ordinal data-Reference-Cited by-同舟云学术

Model selection for mixture-based clustering for ordinal data

Published:2016-12 Issue:4 Volume:58 Page:437-472
ISSN:1369-1473
Container-title:Australian & New Zealand Journal of Statistics
language:en
Short-container-title:Aust. N. Z. J. Stat.

Author:

Fernández D.¹^ORCID,Arnold R.²

Affiliation:

1. Department of Epidemiology & Biostatistics; School of Public Health; University at Albany: State University of New York; One University Place, Room 143 Rensselaer NY 12144-3456 USA

2. School of Mathematics and Statistics; Victoria University of Wellington; Wellington New Zealand

Funder

Victoria University of Wellington

Marsden Fund Council

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1111/anzs.12179/fullpdf

Reference71 articles.

1. Analysis of Ordinal Categorical Data

2. Quasi-symmetric latent class models, with application to rater agreement;Agresti;Biometrics,1993

3. Regression and ordered categorical variables;Anderson;Journal of the Royal Statistical Society: Series B,1984

4. Comparison of Akaike information criterion and consistent Akaike information criterion for model selection and statistical inference from capture-recapture studies;Anderson;Journal of Applied Statistics,1998

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Order Selection in a Finite Mixture of Birnbaum-Saunders Distributions;Austrian Journal of Statistics;2022-08-22

2. Sufficient dimension reduction for clustered data via finite mixture modelling;Australian & New Zealand Journal of Statistics;2022-01-22

3. Model‐based clustering of time‐dependent categorical sequences with application to the analysis of major life event patterns;Statistical Analysis and Data Mining: The ASA Data Science Journal;2021-03-08

4. Model Fit and Comparison in Finite Mixture Models: A Review and a Novel Approach;Frontiers in Education;2021-03-04

5. Assigning scores for ordered categorical responses;Journal of Applied Statistics;2019-10-09