A Bayesian nonparametric model for zero‐inflated outcomes: Prediction, clustering, and causal estimation

Author:

Oganisian Arman1ORCID,Mitra Nandita1ORCID,Roy Jason A.2ORCID

Affiliation:

1. Department of Biostatistics Epidemiology, and Informatics University of Pennsylvania Philadelphia Pennsylvania

2. Department of Biostatistics and Epidemiology Rutgers University Piscataway New Jersey

Funder

American Cancer Society

National Institute of General Medical Sciences

Publisher

Wiley

Subject

Applied Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,Statistics and Probability

Cited by 7 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. Patent Keyword Analysis Using Bayesian Zero-Inflated Model and Text Mining;Stats;2024-08-03

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3. Confounder-dependent Bayesian mixture model: Characterizing heterogeneity of causal effects in air pollution epidemiology;Biometrics;2024-03-27

4. Multi-Dimensional Clustering Based on Restricted Distance-Dependent Mixture Dirichlet Process for Diffusion Tensor Imaging;Journal of Data Science;2024

5. Hybrid Distribution Separation for Prediction of Heterogeneous Zero-Inflated Time Series;2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom);2023-12-21

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