- 时间：10:00 am – 11:00 am
- 日期：Thursday 24th June
- 地点：MB341, Math Building
- 主办方：Department of Statistics and Actuarial Science
- 主讲人：Dr Jigao Yan, Soochow University
In this paper, the complete convergence for maximum of weighted sums of widely negative orthant dependent (WNOD) random variables are investigated. Some sufficient conditions for the convergence are provided and a relationship between the weight and the boundary function is revealed. Additionally, a Marcinkiewicz-Zygmund type strong law of large number for weighted sums of WOND random variables is obtained. The results obtained in this paper generalize some corresponding ones for independent and some dependent random variables. As an application, the strong consistency for the weighted estimator in a non-parametric regression model is established.
Jigao Yan, Ph.D., Associate Professor, visiting scholar in Humboldt Universität zu Berlin, German. He is the Programme Director of Department of Statistics, School of Mathematical Sciences, Soochow University. His research interest includes the limit theory of dependent random variables and its application. He has 20+ papers published on top journals, including JMAA，ACTA Math. Sinica (English Series), Extremes journals. He has completed a series of projects supported by National Natural Science Foundation (NSFC) and Natural Science Foundation of Jiangsu. China. He published a textbook on Probability and Mathematical Statistics by Higher Education Press. He is one member of National Association of Digital Economy and Blockchain Technology, and the Deputy Secretary-General of Suzhou Statistical Research Society, China, and one member of the Graduate Education Steering Committee of The first master's degree of Suzhou University.