아래와 같이 초청특강을 개최하오니 많은 참여 바랍니다.

1. 일시 : 2024년 1월 25일(목), 오후 3시-

2. 장소 : 자연대연구실험동 222호

3. 연사 : 박연희 교수 (University of Wisconsin-Madison, Department of Biostatistics and Medical Informatics)

4. 연제 : Application of envelope models to biomedical data

  The envelope model is first introduced by Cook et al. (2010) as an efficient method to estimate the regression coefficients under the context of multivariate linear regression. It uses sufficient dimension reduction techniques to identify the part of the data that is immaterial to the estimation goal. The subsequent estimation is only based on the material part and is thus more efficient. As a prominent dimension reduction method for multivariate linear regression, the envelope model has received increased attention over the past decade due to its modeling flexibility and success in enhancing estimation and prediction efficiencies. The envelope model has been adapted to many areas. Among the envelope models, the groupwise envelope model (Park et al. 2017), partial predictor envelope model (Park et al. 2022), and Bayesian simultaneous partial envelope model (Shen et al. 2023) are discussed in this talk with the application to Imaging Genetic Analysis for Alzheimer's Disease Neuroimaging Initiative (ADNI) study and Cytokine-based Biomarker Analysis for COVID-19. The application of envelope models shows the effectiveness of the models in estimation and prediction, which leads to a clear scientific interpretation of the results.