School of Arts and Sciences, Statistics

Fingerprint Dive into the research topics where School of Arts and Sciences, Statistics is active. These topic labels come from the works of this organization's members. Together they form a unique fingerprint.

    • Network Recent external collaboration on country level. Dive into details by clicking on the dots.

    Profiles

    No photo of Pierre Bellec

    Pierre Bellec

    Person

    20152019
    No photo of Steven Buyske

    Steven Buyske

    Person

    19892020
    No photo of Javier Cabrera

    Javier Cabrera

    Person

    19852020

    Projects

    EAGER: COLLABORATIVE RESEARCH: MATDAT18 TYPE-I: DEVELOPMENT OF A MACHINE LEARNING FRAMEWORK TO OPTIMIZE REAXFF FORCE FIELD PARAMETERS

    Dasgupta, T.

    National Science Foundation (National Science Foundation (NSF))

    10/1/18 → 9/30/20

    Project: Research project

    • NONPARAMETRIC INFERENCE AND PREDICTION FOR COMPLEX DATA BY DATA DEPTH, CONFIDENCE DISTRIBUTION AND MONTE CARLO METHOD

    Liu, R.

    National Science Foundation (National Science Foundation (NSF))

    8/15/18 → 7/31/21

    Project: Research project

    • INFERENCE IN HIGH-DIMENSIONAL LINEAR MODELS: METHODS, THEORY, AND APPLICATIONS

    Guo, Z.

    National Science Foundation (National Science Foundation (NSF))

    8/15/18 → 7/31/21

    Project: Research project

    • Research Output

    A CD-based mapping method for combining multiple related parameters from heterogeneous intervention trials

    Jiao, Y., Mun, E. Y., Trikalinos, T. A. & Xie, M., 2020, In : Statistics and its Interface. 13, 4, p. 533-549 17 p.

    Research output: Contribution to journal › Article

    Open Access

    • A Generalized Gaussian Process Model for Computer Experiments With Binary Time Series

    Sung, C. L., Hung, Y., Rittase, W., Zhu, C. & Jeff Wu, C. F., Apr 2 2020, In : Journal of the American Statistical Association. 115, 530, p. 945-956 12 p.

    Research output: Contribution to journal › Article

    • 1 Scopus citations

    An advantage actor-critic algorithm with confidence exploration for open information extraction

    Liu, G., Li, X., Sun, M. & Li, P., 2020, Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020. Demeniconi, C. & Chawla, N. (eds.). Society for Industrial and Applied Mathematics Publications, p. 217-225 9 p. (Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

    Open Access
    • 1 Scopus citations