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Accepted (AM)Open Access (License Unspecified), Open
Journal article
Using a latent variable model with non-constant factor loadings to examine PM2.5 constituents related to secondary inorganic aerosols
Statistical modelling, v 16(2)
01 Apr 2016
PMCID: PMC4982519
PMID: 27528825
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Factor analysis is a commonly used method of modelling correlated multivariate exposure data. Typically, the measurement model is assumed to have constant factor loadings. However, from our preliminary analyses of the Environmental Protection Agency's (EPA's) PM2.5 fine speciation data, we have observed that the factor loadings for four constituents change considerably in stratified analyses. Since invariance of factor loadings is a prerequisite for valid comparison of the underlying latent variables, we propose a factor model that includes non-constant factor loadings that change over time and space using P-spline penalized with the generalized cross-validation (GCV) criterion. The model is implemented using the Expectation-Maximization (EM) algorithm and we select the multiple spline smoothing parameters by minimizing the GCV criterion with Newton's method during each iteration of the EM algorithm. The algorithm is applied to a one-factor model that includes four constituents. Through bootstrap confidence bands, we find that the factor loading for total nitrate changes across seasons and geographic regions.
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Details
- Title
- Using a latent variable model with non-constant factor loadings to examine PM2.5 constituents related to secondary inorganic aerosols
- Creators
- Zhenzhen Zhang - University of Michigan–Ann ArborMarie S. O'Neill - University of Michigan–Ann ArborBrisa N. Sanchez - University of Michigan–Ann Arbor
- Publication Details
- Statistical modelling, v 16(2)
- Publisher
- Sage
- Number of pages
- 23
- Grant note
- R01ES016932 / NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Environmental Health Sciences (NIEHS) RD834800; RD83543601 / EPA; United States Environmental Protection Agency R01ES016932; R01ES017022; P01ES022844; P20ES018171 / NIH; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA P30 ES017885 / UM NIEHS Core Center
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000373389000001
- Other Identifier
- 991020100071004721
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- Web of Science research areas
- Statistics & Probability