Colloquium with Xiulin Xie (2025-09-10)

"Online Monitoring of Air Quality Using PCA-Based Sequential Learning"

Xiulin Xie
Department of Statistics,
Florida State University (FSU)

Wednesday, Sep 10, 2025

Nespresso & Teatime - 417 DSL Commons: 03:00 to 03:30 PM Eastern Time (US and Canada)
Colloquium - 499 DSL Seminar Room: 03:30 to 04:30 PM Eastern Time (US and Canada)

Abstract:

Air pollution surveillance is critically important for public health. To monitor a sequential process online, a major statistical tool is statistical process control (SPC) chart. However, traditional SPC charts are developed mainly for monitoring production lines in the manufacturing industry under the assumptions that process observations at different observation times are independent and identically distributed with a parametric (e.g., normal) distribution when the process is stable. Nevertheless, the air pollution and meteorological data would not satisfy these conditions due to serial correlation, seasonality, and other complex data structure. In this talk, we present our latest research on sequential monitoring of air quality over time. In particular, we propose a flexible method for sequential monitoring of high-dimensional dynamic processes with serially correlated data. The new method is based on nonparametric longitudinal modeling for describing the longitudinal pattern of the process under monitoring, principal component analysis for dimension reduction, and a sequential learning algorithm for developing an effective decision rule. Numerical studies and real data applications show that the proposed method works well.