时间：12月28日10：00-11：00
地点：腾讯会议ID：839 618 578
报告人：郑术蓉 教授
主持人：项冬冬 教授
摘要：
In this talk, we propose a scale invariant linear discriminant analysis classifier for high-dimensional data with dense signals. The method is valid for both cases that the data dimension is smaller or greater than the sample size. Based on recent advances of the sample correlation matrix in random matrix theory, we derive the asymptotic limits of the error rate which characterizes the influences of the data dimension and the tuning parameter. The major advantage of our proposed classifier is scale invariant and it is applicable to any variances of the feature. Several numerical studies are investigated and our proposed classifier performs favorably in comparison to some existing methods.
报告人简介：
郑术蓉，东北师范大学教授。主要从事大维随机矩阵理论及高维统计分析的研究。曾在Annals of Statistics, JASA, Biometrika等统计学重要学术期刊上发表多篇学术论文。现任Annals of Statistics、Statistica Sinica等学术期刊编委，曾主持多项国家自然科学基金项目等。