时 间：2025年5月13日 15:30-16:30
地 点：普陀校区理科大楼A614
报告人：范国良 上海海事大学教授
主持人：吴贤毅 华东师范大学教授
摘 要：
Among existing methods for independence test, mutual information (MI) has great popularity as it is invariant to monotone transformations and enjoys higher power in detecting nonlinear associations. In this paper, we propose a novel MI-based independence test in the presence of measurement errors. The conditional density functions involved in MI are estimated using a novel deconvolution double kernel method. The convergence rates of these estimates are derived under the assumption that the measurement errors are either ordinary or super smooth. In addition, the asymptotic behaviors of the resultant estimate of MI are established under both the null and alternative hypotheses. Extensive simulation studies and an application to the low-resolution observations of source stars dataset confirm the superior numerical performances of the proposed methods.
报告人简介：
范国良，男，汉族，安徽黄山人，上海海事大学教授，博士生导师，上海市东方英才，美国《Mathematical Review》评论员，中国现场统计研究会大数据统计分会常务理事，中国现场统计研究会生存分析分会理事，中国现场统计研究会资源与环境统计分会理事，中国工程概率统计学会理事，中国商业统计学会理事。主要从事统计学相关的教学及科研工作，在中国科学数学、Science China Mathematics、Statistica Sinica、Journal of Multivariate Analysis、Statistics and Computing等国内外重要学术刊物上发表学术论文六十余篇。主持国家级和省部级等各类项目十余项。获安徽省科学技术奖、上海市教学成果奖二等奖，上海海事大学教学成果特等奖，上海市一流课程等。