Veridical data science

DOI10.1073/pnas.1901326117zbMath1456.62321arXiv1901.08152OpenAlexW2986518719WikidataQ89701062 ScholiaQ89701062MaRDI QIDQ5854813

No author found.

Publication date: 12 March 2021

Published in: Proceedings of the National Academy of Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1901.08152

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Research data for problems pertaining to statistics (62-11) Statistical aspects of big data and data science (62R07)

Related Items (14)

Interoperability of statistical models in pandemic preparedness: principles and reality ⋮ Deconfounding and Causal Regularisation for Stability and External Validity ⋮ Stable Discovery of Interpretable Subgroups via Calibration in Causal Studies ⋮ What is uncertainty in today's practice of data science? ⋮ Controlling False Discovery Rate Using Gaussian Mirrors ⋮ Regression diagnostics meets forecast evaluation: conditional calibration, reliability diagrams, and coefficient of determination ⋮ Distributionally robust and generalizable inference ⋮ Defining replicability of prediction rules ⋮ Discussion of the Paper “Prediction, Estimation, and Attribution” by B. Efron ⋮ The Data Science Process: One Culture ⋮ Stabilizing Variable Selection and Regression ⋮ Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules ⋮ Discussion of the Paper “Prediction, Estimation, and Attribution” by B. Efron ⋮ The Data Science Process: One Culture

Uses Software

• R
• AdaBoost.MH
• R Markdown
• Jupyter

Cites Work

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