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Generalized Factor Model for Ultra-high Dimensional Correlated Variables with Mixed Types

主讲人: 林华珍
主讲人简介:

林华珍,西南财经大学统计学院教授、博导,统计研究中心主任,美国华盛顿大学生物统计系博士后,四川大学博士。有多篇学术论文发表在The Annals of  Statistics、Journal of the American Statistical Association、Journal of  Econometrics、Journal of the Royal Statistical Society:Series  B (Statistical Methodology)BiometrikaBiometrcs等国际统计学和计量经济学顶级期刊上,先后六次主持国家自然科学基金项目。

林华珍教授是国际IMS-China、IBS-CHINA及ICSA-China委员,中国现场统计研究会数据科学与人工智能分会理事长,第九届全国工业统计学教学研究会副会长,中国现场统计研究会环境与资源分会、高维数据分析分会、生物医学统计学会、生存分析分会等多个分会的副理事长。先后是国际统计学期刊 Biometrics、Journal  of Business & Economic Statistics、Scandinavian Journal of Statistics、Canadian Journal of Statistics、Statistics and Its Interface、Statistical Theory and Related Fields的Associate Editor,国内核心学术期刊《应用概率统计》《系统科学与数学》《数理统计与管理》编委。

主持人: 林明
简介:

As high-dimensional data measured with mixed-type variables gradually become prevalent, it is particularly appealing to represent those mixed-type high-dimensional data using a much smaller set of so-called factors. Due to the limitation of the existing methods for factor analysis that deal with only continuous variables, in this paper, we develop a generalized factor model, a corresponding algorithm and theory for ultra-high dimensional mixed types of variables where both the sample size $n$ and variable dimension $p$ could diverge to infinity. Specifically, to solve the computational problem arising from the non-linearity and mixed types, we develop a two-step algorithm so that each update can be carried out in parallel across variables and samples by using an existing package. Theoretically, we establish the rate of convergence for the estimators of factors and loadings in the presence of nonlinear structure accompanied with mixed-type variables when both $n$ and $p$ diverge to infinity. Moreover, since the correct specification of the number of factors is crucial to both the theoretical and the empirical validity of factor models, we also develop a criterion based on a penalized loss to consistently estimate the number of factors under the framework of a generalized factor model. To demonstrate the advantages of the proposed method over the existing ones, we conducted extensive simulation studies and also applied it to the analysis of the NFBC1966 dataset and a cardiac arrhythmia dataset, resulting in more predictive and interpretable estimators for loadings and factors than the existing factor model.

时间: 2022-07-04(Monday)16:30-18:00
地点: 线上腾讯会议
讲座语言: 中文
期数:
主办单位: 厦门大学经济学院、王亚南经济研究院、邹至庄经济研究院
承办单位: 厦门大学经济学院统计学与数据科学系
类型:
独立讲座
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