报告题目：A Novel Method for Modeling QQ Data

摘 要：In many scientific areas, data with mixed quantitative and qualitative (QQ) responses are commonly encountered with a large number of predictors. By exploring the association between QQ responses, existing approaches often consider a joint model of them. However, the dependency among predictive variables also provides useful information for fitting QQ responses. Hence in this work, we propose a novel approach to jointly model the QQ responses by incorporating the dependency information of predictors. The proposed method is computationally efficient and provides accurate parameter estimation under a penalized likelihood framework. Moreover, the asymptotically theoretical results of the proposed method are established under some regularity conditions. The performance of the proposed method is examined through simulations and real case studies in material science and genetics.

报告时间：2024年10月12日下午3:30--4:30

报告地点：统计与数据科学学院109会议室

主办单位：统计与数据科学学院

专家简介：康晓宁，统计学博士，教授，硕士生导师，应用统计(商业分析)教研室主任，东北财经大学杰出学者，获“毕业生心目中最有影响力的恩师”称号。本科、硕士毕业于大连理工大学数学系，博士毕业于美国弗吉尼亚理工大学统计系。主要研究领域包括高维图模型、混合数据建模、模型平均等。曾先后主持和参与国家自然科学基金、教育部人文社科基金、辽宁省自然科学基金等项目。其研究成果已发表在Statistica Sinica, Technometrics, Journal of Multivariate Analysis, International Statistical Review等国际著名期刊上。