BiMM forest: A random forest method for modeling clustered and longitudinal binary outcomes-Reference-Cited by-同舟云学术

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BiMM forest: A random forest method for modeling clustered and longitudinal binary outcomes

Published:2019-02 Issue: Volume:185 Page:122-134
ISSN:0169-7439
Container-title:Chemometrics and Intelligent Laboratory Systems
language:en
Short-container-title:Chemometrics and Intelligent Laboratory Systems

Author:

Speiser Jaime Lynn,Wolf Bethany J.,Chung Dongjun^ORCID,Karvellas Constantine J.^ORCID,Koch David G.,Durkalski Valerie L.

Funder

NIH/NIDDK

NIH/NCATS

NIH/NIAMS

NIH/NIGMS

NIH/NCI

Publisher

Elsevier BV

Subject

Spectroscopy,Process Chemistry and Technology,Computer Science Applications,Software,Analytical Chemistry

Reference41 articles.

1. BiMM tree: a decision tree method for modeling clustered and longitudinal binary outcomes;Speiser;Commun. Stat. Simulat. Comput.,2018

2. Random forests;Breiman;Mach. Learn.,2001

3. The Elements of Statistical Learning;Hastie,2009

4. Do we need hundreds of classifiers to solve real world classification problems;Fernández-Delgado;J. Mach. Learn. Res.,2014

5. Acute liver failure: summary of a workshop;Lee;Hepatology,2008

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