Imprecise Learning from Misclassified and Incomplete Categorical Data with Unknown Error Structure

Omar, Aziz und Augustin, Thomas (2023): Imprecise Learning from Misclassified and Incomplete Categorical Data with Unknown Error Structure. 10th International Conference on Soft Methods in Probability and Statistics (SMPS), Valladolid, Spain, 14. -16. September 2022. In: Building Bridges between Soft and Statistical Methodologies for Data Science, Advances in Intelligent Systems and Computing Vol. 1433 Cham: Springer. pp. 295-302

Abstract

This article addresses the problem of learning from potentially misclassified and incomplete categorical data when the error structure is unknown and no prior information about the distribution of the data is available. We propose to use the knowledge gained from the well-known practice of double sampling to accomplish two goals; First, we estimate the unknown error structure. Then, under the framework of imprecise probability, we derive a prior Dirichlet distribution that expresses a state of quasi-near-ignorance about the data. Updating this prior using sample data leads to a quasi-near-ignorance posterior distribution that produces non-trivial estimates.

• Item Type: Conference or Workshop Item (Paper)
• Faculties: Mathematics, Computer Science and Statistics > Statistics > Chairs/Working Groups > Foundations of Statistics and their Applications
• Subjects: 000 Computer science, information and general works > 000 Computer science, knowledge, and systems; 500 Science > 510 Mathematics
• ISBN: 978-3-031-15509-3 ; 978-3-031-15508-6
• ISSN: 2194-5357
• Place of Publication: Cham
• Language: English