科学研究

山东大学中泰证券金融研究院教授、博士生导师。教育部应用统计专业硕士教育指导委员会成员，山东省政府参事。从事大数据、高维统计、非参数和半参数统计以及金融统计等方的研究，在国际统计学、机器学习和相关应用学科顶级期刊(包括Annals of Statistics, Journal of Machine Learning Research, 中国科学)和其它重要期刊发表研究论文120余篇；多个资政报告得到主管省长的重要批示。主持过多项国家自然科学基金课题、博士点专项基金课题、山东省自然科学基金重点项目等；获得国家统计局颁发的统计科技进步一等和二等奖(排名第一)，山东省优秀教学成果一等奖(排名第一)。

A basic condition for efficient transfer learning is the similarity between the target model and relevant source models. In practice, however, the similarity condition is difficult to meet or is often violated. In this paper, under the framework of exponential family with heterogeneous source models, the related models are precisely combined by bran-new measures: linear correlation ratios between the target model and source models. Based on this type of combinations, the precision transfer likelihood is constructed by the target likelihood combined with the transferred likelihoods from the source models. Methodologically, some techniques are suggested for transferring the information from simple source models to a relatively complex target model. Theoretically, the asymptotic properties, including the standard convergence rate, are achieved, even for the case where the source models are unrelated to the target model. It can be seen from the theories and numerical results that the inference on the target model is significantly improved by the information from source models, and it is somewhat surprising that phenomenon of Stein's paradox is illustrated.