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    【学术讲座】Generalized Linear Models with External Auxiliary Information in the Presence of Population Heterogeneity

    2026-06-09  点击:[]

    报告题目:Generalized Linear Models with External Auxiliary Information in the Presence of Population Heterogeneity

    报告人:赵亦川

    邀请人:郑海涛

    时间:2026年6月11日(周四)11:00-12:00

    地点:X30423

    报告摘要:

    Generalized linear models (GLMs) are highly effective for modeling mean responses under nonstandard conditions, accommodating both discrete and continuous data distributions. In the analysis of individual-level data, numerous parameter estimation methods for GLMs have been developed, with the quasi-likelihood method being particularly notable. Recently, leveraging auxiliary information from external sources to improve the estimation efficiency of model parameters has gained significant attention in statistical research. In this paper, we construct estimating equations using auxiliary information from external sources and combine them with quasi-likelihood estimating equations derived from individual-level data to form a unified estimating equation. To address the population heterogeneity among different sources of information, we define bias parameters and include them in the unified estimating equation. We utilize a generalized method of moments (GMM) based on the unified estimating equation to estimate the coefficients in GLMs. Simultaneously, an adaptive LASSO penalty term is introduced to characterize the potential population heterogeneity among different sources of information. We also extend the proposed method to handle scenarios involving missing data. Simulation studies are conducted under various settings to examine the finite sample properties of the proposed method and compare its performance with that of the quasi-likelihood method. We also demonstrate the large sample properties of the proposed method and provide proofs for them. Finally, we apply the proposed method to analyze a real world dataset.

    The talk is based on joint work with Dazhi Zhao.

    主讲人简介:赵亦川博士是美国佐治亚州立大学的教授,主要研究方向为生存分析、经验似然方法、非参数统计、ROC曲线分析、生物信息学、蒙特卡洛方法和模糊系统等统计模型。赵教授在统计学和生物统计学研究领域发表了一百多篇研究论文,在施普林格出版社编辑出版七本书籍,在全球各地作了两百多次的学术报告,多次成功举办了统计学,生物统计学和生物信息学方面的大型国际学术会议。赵教授目前是若干权威统计期刊的副主编或编委会成员,也是泛华统计协会与 Springer 出版社合作的统计学丛书的共同主编。他目前担任泛华统计协会的董事会成员,也是美国统计学会的会士和国际统计学会的当选成员。

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