Cross-Study Replicability in Cluster Analysis-Reference-Cited by-同舟云学术

Cross-Study Replicability in Cluster Analysis

Published:2023-05-01 Issue:2 Volume:38 Page:
ISSN:0883-4237
Container-title:Statistical Science
language:
Short-container-title:Statist. Sci.

Author:

Masoero Lorenzo¹,Thomas Emma²,Parmigiani Giovanni³,Tyekucheva Svitlana⁴,Trippa Lorenzo⁵

Affiliation:

1. Lorenzo Masoero is Applied Scientist at Amazon. This research work was conducted prior to this position

2. Emma Thomas is Data Scientist at RAND

3. Giovanni Parmigiani is Professor in the Departments of Data Science, Dana-Farber Cancer Institute and Biostatistics, Harvard T.H. Chan School of Public Health

4. Svitlana Tyekucheva is Senior Research Scientist, Department of Data Science, Dana-Farber Cancer Institute and Department of Biostatistics, Harvard T.H. Chan School of Public Health

5. Lorenzo Trippa is Associate Professor, Department of Data Science, Dana-Farber Cancer Institute and Department of Biostatistics, Harvard T.H. Chan School of Public Health

Publisher

Institute of Mathematical Statistics

Subject

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

Reference51 articles.

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4. TRIPPA, L., WALDRON, L., HUTTENHOWER, C. and PARMIGIANI, G. (2015). Bayesian nonparametric cross-study validation of prediction methods. Ann. Appl. Stat. 9 402–428.

5. MURDOCH, W. J., SINGH, C., KUMBIER, K., ABBASI-ASL, R. and YU, B. (2019). Definitions, methods, and applications in interpretable machine learning. Proc. Natl. Acad. Sci. USA 116 22071–22080.

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