報告題目：Bayesian Regularization Methods For (Unspecified) Loadings in Partially Confirmatory Factor Analysis
報 告 人：付志慧 教授 閩南師範大學
報告時間：2024 年 8 月 4 日上午 8:00-9:00
報告地點：數學樓第二報告廳
校内聯系人：朱複康 fzhu@jlu.edu.cn
報告摘要：The application of regularization methods to factor analysis models is increasingly gaining popularity. This research proposes more valid Gibbs sampling algorithms based on the Partially Confirmatory Factor Analysis (PCFA) framework for unspecified loadings. Four Bayesian regularization methods were employed and compared, including Lasso, Spike-and-Slab prior (SSP), Horseshoe, and Horseshoe+. Simulation study results indicate that compared to the original Lasso in PCFA, the other three priors demonstrate better validity and robustness. In particular, SSP shows better parameter recovery performance even under extreme conditions. Moreover, all three priors showed better identification of factor correlation, Horseshoe+ and SSP showed better robustness at the exploratory and confirmatory step, respectively. Finally, results from real-data demonstrate that the Horseshoe provides the most parsimonious factor structure. The four different priors also offer developers more flexible options in empirical research.
報告人簡介：付志慧，博士，閩南師範大學數學與統計學院碩士生導師，校龍江學者特聘教授，入選福建漳州市高層次 D類人才、沈陽市第一批高層次人才-拔尖人才，美國伊利諾伊大學香槟分校訪問學者。主要研究方向為貝葉斯統計、教育統計與心理測量等。主持完成國家自然科學基金項目、國家社會科學基金項目、遼甯省自然科學基金和福建省自然科學基金項目、全國統計科學研究項目、遼甯省教育廳項目等課題；獲第十一屆全國統計科學研究優秀成果二等獎、遼甯省自然科學技術成果三等獎；科學出版社獨立出版專著1部，在《British Journal of Mathematical and Statistical Psychology》、《Physica A: Statistical Mechanics and its Application》、《Multivariate Behavioral Research》等期刊上發表20餘篇學術論文。兼全國工業統計學教學研究會青年統計學家協會常務理事、中國教育學會教育統計與測量分會理事、福建省統計學會常務理事等。
