University of Illinois Chicago
Browse

A Systematic View of Information-Based Optimal Subdata Selection: Algorithm Development, Performance Evaluation, and Application in Financial Data

Download (589.64 kB)
journal contribution
posted on 2023-05-05, 05:25 authored by Min YangMin Yang, L He, William Li, Difan Song
No description supplied

Funding

Collaborative Research: Design-Based Optimal Subdata Selection Using Mixture-of-Experts Models to Account for Big Data Heterogeneity | Funder: National Science Foundation | Grant ID: DMS-2210546

Collaborative Research: Information-based subdata selection inspired by optimal design of experiments | Funder: National Science Foundation | Grant ID: DMS-1811291

History

Citation

Yang, M., He, L., Li, W.Song, D. (2022). A Systematic View of Information-Based Optimal Subdata Selection: Algorithm Development, Performance Evaluation, and Application in Financial Data. Statistica Sinica. https://doi.org/10.1080/19466315.2020.1841023

Publisher

Academia Sinica

issn

1017-0405

Usage metrics

    Categories

    Keywords

    Statistics & Probability4905 StatisticsStatisticsOther Mathematical SciencesArtificial Intelligence and Image ProcessingStatisticsArtificial Intelligence and Image Processing

    Licence

    In Copyright

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC