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Nonparametric Likelihood and Its Applications

Tuesday, March 17, 2015 - 11:00
Junjian Zhang - Professor, Guangxi Normal University, China
Statistics Seminar
Room 4192, Earth Science Buildling, 2207 Main Mall

Junjian Zhang

Dr. Junjian Zhang is a professor at Guangxi Normal University, Guilin China. He received his PhD in 2006 at Academy of Mathematics and Systems Science, Chinese Academy of Sciences under the supervision of Professor Guoying Li. Following his PhD, he conducted post-doctoral research at Beijing University of Technology under supervision of Professor Zhongzhan Zhang. He is currently visiting the department of statistics, UBC hosted by Dr. Jiahua Chen for the period of 2015. His research interests include mathematical statistics and its applications, especially in nonparametric likelihood ratio. His research is supported by the following funds: National Social Science Foundation of China, National Natural Science Foundation of China, Guangxi Science Foundation. He was the winner of the “best paper award of Zhong Jiaqing” at the 10th Jingjin Wusi youth meeting and the “best paper” award at the 8th Guangxi statistical science colloquium. He was invited speaker in many research conferences including the united meeting of Hunan,Guangdong and Guangxi mathematical societies, the Tenth National Congress of Chinese Society of Probability and Statistics. His email address is jjzhang [at]

Nonparametric Likelihood and Its Applications

Show Abstract

Nonparametric likelihood is one of the important topics in statistics. In this talk, we will introduce the basic ideas for nonparametric likelihood and present our latest research achievements. For example, we generalize the empirical likelihood to the empirical Lq likelihood and Empirical power divergent likelihood. The former is usually used to the estimating theory, the latter is usually used to the goodness-of-fit. This talk will focus on the goodness-of-fit. In addition, the talk will discuss the adjusted empirical (Euclidean) likelihood and its applications, the nonparametric likelihood for the complex data such as the rounded data, dependent data, high-dimensional data, and so on.