- 標題：2023 年 4 月 21 日 CRETA Seminar - Covariate adjusted functional principal component analysis
- 公告日期：2023-03-30
【 4 月 21 日 CRETA Seminar】
日期：2023 年 4 月 21 日 (週五) 下午 2:00~3:30
地點：線上舉行
講者：江其衽教授 (國立臺灣大學統計與數據科學研究所)
演講主題：Covariate adjusted functional principal component analysis
講題摘要：
Principal component analysis is a classical dimension reduction tool in multivariate statistical analysis and its extension to functional data, termed Functional Principal Component Analysis (FPCA), plays a central role in the analysis of samples that are curves, functions, or surfaces. When additional covariates are available, three major models could be considered to accommodate them into the framework of FPCA. The first model integrates the covariates in the mean function only, and the second model integrates them in both the mean and the covariance function. However, the first model is not suitable for data that display second-order variation, while the second model makes it difficult to perform subsequent statistical analyses on the dimension-reduced representations in addition to being time-consuming. The third model was proposed to tackle these issues. Specifically, it assumes the covariance function varies with the covariates via its eigenvalues while the corresponding eigenfunctions remain independent of the covariates. In addition to briefly introducing the proposed estimators for all three models, I will use different real examples to demonstrate their performance.
講者介紹：
江其衽教授 (國立臺灣大學統計與數據科學研究所)
備註事項：
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報名網址：https://www.creta.org.tw/
報名期限：2023/4/20 (四) 中午 12:00
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- 最後修改時間：2023-04-07 AM 11:54
