Publication Type
Journal Article
Version
publishedVersion
Publication Date
8-2024
Abstract
The power of data and correct statistical analysis has never been more prevalent. Academics and practitioners require nowadays an accurate application of quantitative methods. Yet many branches are subject to a crisis of integrity, which is shown in an improper use of statistical models, p-hacking, HARKing, or failure to replicate results. We propose the use of a Peer-to-Peer (P2P) ecosystem based on a blockchain network, Quantinar, to support quantitative analytics knowledge paired with code in the form of Quantlets or software snippets. The integration of blockchain technology allows Quantinar to ensure fully transparent and reproducible scientific research.
Keywords
Blockchain, Machine learning, dao, p2p, e-Learning
Discipline
Data Science
Publication
Computational Statistics
First Page
1
Last Page
36
ISSN
0943-4062
Identifier
10.1007/s00180-024-01529-7
Publisher
Springer
City or Country
Cham
Embargo Period
9-3-2024
Citation
BAG, Raul; SPILAK, Bruno; WINKEL, Julian; and HARDLE, Wolfgang Karl.
Quantinar: A blockchain peer-to-peer ecosystem for modern data analytics. (2024). Computational Statistics. 1-36.
Available at: https://ink.library.smu.edu.sg/skbi/45
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s00180-024-01529-7