Mixture of Networks for Clustering Categorical Data: A Penalized Composite Likelihood Approach-Reference-Cited by-同舟云学术

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Mixture of Networks for Clustering Categorical Data: A Penalized Composite Likelihood Approach

Published:2022-11-17 Issue: Volume: Page:1-15
ISSN:0003-1305
Container-title:The American Statistician
language:en
Short-container-title:The American Statistician

Author:

Baek Jangsun¹^ORCID,Park Jeong-Soo¹^ORCID

Affiliation:

1. Department of Statistics, Chonnam National University, Gwangju, Korea

Funder

Ministry of Education and National Research Foundation of Korea

National Research Foundation of Korea

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,General Mathematics,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/00031305.2022.2141856

Reference44 articles.

1. III.—A Series Formula for the Roots of Algebraic and Transcendental Equations

2. Model-based clustering, classification, and discriminant analysis via mixtures of multivariate t-distributions

3. Mixtures of common t-factor analyzers for clustering high-dimensional microarray data

4. A novel attribute weighting algorithm for clustering high-dimensional categorical data

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