题目：Research on Time Series Data Classification Based on Variational Mode Decomposition and Deep Forest

摘要：In this paper, a new adaptive decomposition and deep forest classification method is constructed based on nonlinear and non-stationary time series data. Firstly, the regularized variational mode decomposition method is optimized by the improved Harris Hawk algorithm. Secondly, the variable selection method of adaptive elastic net is implemented by the minimum common redundancy maximum relevance criterion. Finally, the two-stage weighted deep forest classification algorithm is constructed. The feasibility of the proposed method is verified by the simulation data, and it is applied to Epileptic Electroencephalogram signals recognition and rolling bearing fault diagnosis. The effectiveness of the proposed method is proved by comparative analysis and statistical test. The proposed method provides theoretical foundation for the analysis of complex nonlinear non-stationary time series data.

简介：秦喜文，长春工业大学大数据科学研究院院长兼校学科办副主任，教授，博士生导师。吉林省第七批拔尖创新人才，省工业与应用数学学会副理事长、省运筹学会常务理事、省现场统计研究会理事、省数学学会理事等。主要研究方向为机器学习、经济统计、大数据分析与智能优化等。主持国家自然科学基金项目3项，省部级项目8项。曾获省科技进步二等奖1项，省自然科学三等奖1项，省专利优秀奖1项。出版专著1部，教材2部，发表SCI、EI等学术论文33篇。

题目：Graph-based two-sample tests for multivariate repeated measurements of histogram objects

摘要：Repeated observations have become increasingly common in biomedical research and longitudinal studies. For instance, wearable sensor devices are deployed to continuously track physiological and biological signals from each individual over multiple days. It remains of great interest to appropriately evaluate how the daily distribution of biosignals might differ across disease groups and demographics. Hence, these data could be formulated as multivariate complex object data, such as probability densities, histograms, and observations on a tree. Traditional statistical methods would often fail to apply, as they are sampled from an arbitrary non-Euclidean metric space. In this talk we propose novel, nonparametric, graph-based two-sample tests for object data with the same structure of repeated measures. We treat the repeatedly measured object data as multivariate object data, which requires the same number of repeated observations per individual but eliminates any assumptions on the errors of the repeated observations. A set of test statistics are proposed to capture various possible alternatives. We derive their asymptotic null distributions under the permutation null. These tests exhibit substantial power improvements over the existing methods while controlling the type I errors under finite samples as shown through simulation studies. The proposed tests are demonstrated to provide additional insights on the location, inter- and intra-individual variability of the daily physical activity distributions in a sample of studies for mood disorders.

简介：张静茹，复旦大学大数据学院青年副研究员，2019年博士毕业于北京大学。研究方向是应用统计、生物统计，研究兴趣包括复杂对象数据的统计分析、移动健康数据分析等。她的工作已发表在Annals of Applied Statistics，Biometrics，Statistica Sinica等国际期刊。

题目：Block-Diagonal Test for High-Dimensional Covariance Matrices

摘要：The testing structure of a high-dimensional covariance matrix plays an important role in financial stock analyses, genetic series analyses, and many other fields. Testing that the covariance matrix is block-diagonal under the high-dimensional setting is a main focus of this paper. To tackle this problem, test procedures that are powerful under normality assumptions, two-diagonal block assumptions or sub-block dimensionality assumptions have been proposed in several existing studies. To relax these conditions, a test framework based on U-statistics is proposed in this paper, and the asymptotic distributions of those U-statistics are established under the null and alternative hypotheses. Moreover, another test approach is developed for alternatives with different sparsity levels. Finally, both a simulation study and real data analysis are conducted to show the performance of our proposed test procedures.

简介：王潇逸，北京师范大学珠海校区统计与数据科学研究中心讲师，毕业于东北师范大学，主要研究领域为高维统计推断、大维随机矩阵理论等。目前，在Statistica Sinica，TEST等国际统计期刊上发表论文多篇。

题目：High-dimensional scale invariant discriminant analysis

摘要：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.

简介：李铭，2012年9月-2016年6月，山东师范大学毕业，获得理学学士学位。2016年9月-2019年6月，山东师范大学毕业，获得统计学硕士学位。现就读于东北师范大学数学与统计学院，统计学专业博士在读。主要研究领域为高维数据分析，大维随机矩阵理论。