Scholarly Colloquia and Events

  • 9/7 Statistics Colloquium, Jae-Kwang Kim

    In-person

    STATISTICS COLLOQUIUM

     

    Jae-Kwang Kim

    LAS Dean’s Professor

    Department of Statistics

    Iowa State University

     

    Multiple bias calibration for valid statistical inference with selection bias

    Abstract

    Valid statistical inference is notoriously challenging when the sample is subject to selection bias. We approach this difficult problem by employing multiple candidate models for the propensity score function combined with empirical likelihood. By incorporating the multiple propensity score (PS) models into the internal bias calibration constraint in the empirical likelihood setup, the selection bias can be safely eliminated so long as the multiple candidate models contain the true PS model. The bias calibration constraint for the multiple PS model in the empirical likelihood is called the multiple bias calibration. The multiple PS models can include both ignorable and nonignorable models. In the context of data integration setup, the conditions for multiple bias calibration can be achieved. Asymptotic properties are discussed, and some limited simulation studies are presented to compare with the existing methods.

     

    Key reference:

     

    Qin, J., D. Leung and J. Shao (2002), ‘Estimation with survey data under
    non-ignorable nonresponse or informative sampling’, Journal of the
    American Statistical Association     97, 193–200.

     

    Morikawa, K. and Kim, J.K. (2021), ‘Semiparametric optimal estimation with nonignorable nonresponse data’, The Annals of Statistics 49(5), 2991–3014.

     

    Han, P. and L. Wang (2013), ‘Estimation with missing data: Beyond
    double robustness’, Biometrika 100, 417–430

     

    Bio: Dr. Jae-kwang Kim is a LAS dean’s professor in the Department of Statistics at Iowa State University (ISU). He got his Ph.D. from ISU in 2000 under the supervision of prof. Wayne Fuller and then worked in various places (such as Westat and Yonsei university in Korea) before he joined ISU in 2008. He is a fellow of ASA and IMS and the president-elect of KISS (Korean International Statistical Society). He has extensive research/consulting experience in the area of survey sampling and missing data analysis.  He is also a co-author of the book “Statistical methods for handling incomplete data (2nd edn)” coauthored with Jun Shao.

     

    Philip E. Austin Building, Rm. 434
    Wednesday, September 7, 2022

    4:00 pm EST, 1-hour duration

    For more information, contact: Tracy Burke at tracy.burke@uconn.edu