A Bayesian regression tree approach to identify the effect of nanoparticles’ properties on toxicity profiles

Cecile Low-Kam, Donatello Telesca, Zhaoxia Ji, Haiyuan Zhang, Tian Xia, Jeffrey I. Zink, Andre E. Nel

Open source

DOI: 10.1214/14-aoas797
Published: 2015-03-01
Container: The Annals of Applied Statistics
Publisher: Institute of Mathematical Statistics
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1214/14-aoas797,
  title = {A Bayesian regression tree approach to identify the effect of nanoparticles’ properties on toxicity profiles},
  author = {Cecile Low-Kam and Donatello Telesca and Zhaoxia Ji and Haiyuan Zhang and Tian Xia and Jeffrey I. Zink and Andre E. Nel},
  year = {2015},
  journal = {The Annals of Applied Statistics},
  doi = {10.1214/14-aoas797},
  url = {https://doi.org/10.1214/14-aoas797}
}

RIS

TY  - JOUR
TI  - A Bayesian regression tree approach to identify the effect of nanoparticles’ properties on toxicity profiles
AU  - Cecile Low-Kam
AU  - Donatello Telesca
AU  - Zhaoxia Ji
AU  - Haiyuan Zhang
AU  - Tian Xia
AU  - Jeffrey I. Zink
AU  - Andre E. Nel
PY  - 2015
JO  - The Annals of Applied Statistics
DO  - 10.1214/14-aoas797
UR  - https://doi.org/10.1214/14-aoas797
ER  -

APA

Low-Kam, C., Telesca, D., Ji, Z., Zhang, H., Xia, T., Zink, J. I., & Nel, A. E. (2015). A Bayesian regression tree approach to identify the effect of nanoparticles’ properties on toxicity profiles. The Annals of Applied Statistics. https://doi.org/10.1214/14-aoas797

Source records

crossref · retrieved 2026-09-26T03:47:44.471Z