应304am永利集团官网李朋副教授邀请, 北京航空航天大学数学科学学院陈迪荣教授, 将于5 月26号(星期四)上午9:00在线举办学术报告.

报告题目：Adaptive estimation for functional data: Using a framelet block-thresholding method

报告时间：5月26号(星期四)上午9:00

腾讯会议ID: 553-876-294

报告摘要: Nonparametric estimation of mean and covariance functions based on discretely observed data is important in functional data analysis. In this talk, we propose a framelet block-thresholding method for estimating mean and covariance functions from discretely sampled noisy observations. Estimated convergence rates are established for all types of sampling schemes. In particular, the results reveal a phase transition phenomenon related to the number of observations on each curve. The procedures are adaptive in automatically adjusting the smoothness properties of the underlying mean and covariance functions. In contrast, theoretical results for other smoothing methods hold in the setting where smoothness parameters are assumed to be known, since the regularization parameters of estimators that depend on smoothness properties need to be chosen carefully. Simulation studies and real data examples are provided to offer empirical support for the theoretical results. A comparison with other methods demonstrates that the proposed method outperforms in adaptivity.

报告人简介

陈迪荣，北京航空航天大学数学科学学院教授, 北航“蓝天学者”特聘教授,博士生导师.1982年1月获学士学位(华中师范大学),1992年7月获博士学位(北京师范大学). 主要从事统计学习理论,函数型数据分析, 以及在国防工程技术中应用研究,取得了具有国际先进水平的成果. 获教育部2012年度自然科学二等奖. 先后主持国家自然科学基金8项,“863”课题3项,“973”计划子课题1项. 发表SCI论文数十篇, 其中多篇发表在权威刊物Appl. Comput. Harmonic Anal., Found. Comput. Math., SIAM J. Math. Anal., SIAM J. Numer. Anal., IEEE Trans. Automatic Control, IEEE Trans Inform. Theory,J. Machine Learn. Research等上,单篇论文被SCI引用最高近两百次.

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