Time:
July 11, 2020 (Saturday) 9:30-10:30 AM
Venue:
Zoom Conference ID: 615 3970 4192
Speaker:
Professor Tang Niansheng, National Outstanding Youth, Changjiang Scholar, dean of School of Mathematics and Statistics, Yunnan University.
Summary:
Block-wise missing data are becoming increasingly common in high dimensional biomedical, social, psychological, and environmental studies. As a result, we need efficient dimension-reduction methods for extracting important information for predictions under such data. Existing dimension-reduction methods and feature combinations are ineffective for handling block-wise missing data. We propose a factor-model imputation approach that targets block-wise missing data, and use an imputed factor regression for the dimension reduction and prediction. Specifically, we first perform screening to identify the important features. Then, we impute these features based on the factor model, and build a factor regression model to predict the response variable based on the imputed features. The proposed method utilizes the essential information from all observed data as a result of the factor structure of the model. Furthermore, the method remains efficient even when the proportion of block-wise missing is high. We show that the imputed factor regression model and its predictions are consistent under regularity conditions. We compare the proposed method with existing approaches using simulation studies, after which we apply it to data from the Alzheimer’s disease Neuroimaging Initiative. Our numerical results confirm that the proposed method outperforms existing competitive approaches.
Brief introduction of the speaker:
Tang Niansheng, PhD, winner of the National Science Fund for Distinguished Young Scholars, Yangtze River Scholars, and New Century Outstanding Talents of the Ministry of Education, Leading Talents in Science and Technology of Yunnan Province, the first batch of Yunling scholars in Yunnan Province, young and middle-aged scholar and technical leader in Yunnan Province, outstanding teacher in Yunnan Province, member of the Economic and Management Discipline Evaluation Group of Yunnan Academic Degrees Committee, and doctoral supervisor. The head of the Key Laboratory of Statistics and Information Technology in Yunnan University, and the leader of the provincial innovation team Research on the Statistical Inference Method of Complex Data in Yunnan University.
