报告题目一：Use of random integration to test equality of high dimensional covariance matrices
主讲人：姜云卢副教授（暨南大学）
时间：2022年6月6日（周一）8:30 a.m.
形式：线上讲座（腾讯会议）
会议ID：264-519-690
主办单位：统计与数学学院
摘要：Testing the equality of two covariance matrices is a fundamental problem in statistics, and especially challenging when the data are high-dimensional. Through a novel use of random integration, we can test the equality of high-dimensional covariance matrices without assuming parametric distributions for the two underlying populations, even if the dimension is much larger than the sample size. The asymptotic properties of our test for arbitrary number of covariates and sample size are studied in depth under a general multivariate model. The finite-sample performance of our test is evaluated through numerical studies. The empirical results demonstrate that our test is highly competitive with existing tests in a wide range of settings. In particular, our proposed test is distinctly powerful under different settings when there exist a few large or many small diagonal disturbances between the two covariance matrices.
主讲人简介：
姜云卢，暨南大学经济学院统计学系副教授、博士生导师。2012年博士毕业于中山大学数学与计算科学学院。目前的主要研究包括：稳健统计、高维数据分析、变量选择和混合模型，至今已公开在JASA、Technometrics、Statistica Sinica等国内外知名期刊上发表SCI论文30余篇，其中入选ESI前1%高被引论文1篇；主持国家自然科学基金项目2项、省部级项目4项和广东省高等教育教学研究改革项目1项；入选“暨南双百英才计划”暨南杰青第一层次和第二层次；入选广东省高等学校“千百十工程”第八批培养对象；荣获第八次广东省统计科研优秀成果奖一等奖（排第三）。
报告题目二：The Poisson Item Count Technique and its non-compliance design for survey with sensitive questions
主讲人：吴琴博士（华南师范大学）
时间：2022年6月6日（周一）10:30 a.m.
形式：线上讲座（腾讯会议）
会议ID：264-519-690
主办单位：统计与数学学院
摘要：The Poisson item count technique (PICT) is a survey method that was recently developed to elicit respondents’ truthful answers to sensitive questions. It simplifies the well-known item count technique (ICT) by replacing a list of independent innocuous questions in known proportions with a single innocuous counting question. However, ICT and PICT both rely on the strong “no design effect assumption” (ie, respondents give the same answers to the innocuous items regardless of the absence or presence of the sensitive item in the list) and “no liar” (ie, all respondents give truthful answers) assumptions. To address the problem of self-protective behavior and provide more reliable analyses, we introduced a noncompliance parameter into the existing PICT. Based on the survey design of PICT, we considered more practical model assumptions and developed the corresponding statistical inferences. Simulation studies were conducted to evaluate the performance of our method. Finally, a real example of automobile insurance fraud was used to demonstrate our method.
主讲人简介：
吴琴，博士，毕业于香港浸会大学统计系，现于华南师范大学统计系工作, 讲师。现主持国家自然科学基金面上项目1项（在研），青年项目1项(已结题)，广东省质量工程项目1项(已结题)。
