Analyzing the effects of air pollution and mortality by generalized additive models with robust principal components

Yaping Wang, Hoang Pham

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

In this paper, we propose generalized additive models (GAMs) based on the robust principal component analysis (PCA) methods, to quantify the association between daily mortality and air pollutant concentrations, especially PM10, CO, NO2, SO2 and O3, for confounding effects of long-term time trend, seasonality, weekday, and meteorological factors. The two PCA methods that will be applied into the GAM are: one is classic PCA (CPCA) and the other is robust PCA (RPCA) with minimum covariance determinant, called CPCA-GAM and RPCA-GAM, respectively. Comparing the analyses between GAM, CPCA-GAM, and RPCA-GAM, we can reach to the conclusions as follows: (1) results from CPCA-GAM and RPCA-GAM are consistent with each other; (2) RPCA is much more effective tool to detect outliners than CPCA; and (3) because PCA eliminates the collinearity between covariates, the coefficients of air pollutants have shown to be more significant than GAM without PCA.

Original languageEnglish (US)
Pages (from-to)253-259
Number of pages7
JournalInternational Journal of System Assurance Engineering and Management
Volume2
Issue number3
DOIs
StatePublished - Sep 2011

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Strategy and Management

Keywords

  • Air pollutants
  • Daily mortality
  • Generalized additive model
  • Minimum covariance determinant
  • Principal component analysis
  • Relative risk

Fingerprint

Dive into the research topics of 'Analyzing the effects of air pollution and mortality by generalized additive models with robust principal components'. Together they form a unique fingerprint.

Cite this