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Probabilistic predictive principal component analysis for spatially misaligned and high‐dimensional air pollution data with missing observations
Accurate predictions of pollutant concentrations at new locations are often of interest in air pollution studies on fine particulate matters (PM(2.5)), in which data is usually not measured at all study locations. PM(2.5) is also a mixture of many different chemical components. Principal component a...
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| Publicat a: | Environmetrics |
|---|---|
| Autors principals: | , , |
| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
2019
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| Matèries: | |
| Accés en línia: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7313548/ https://ncbi.nlm.nih.gov/pubmed/32581624 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/env.2614 |
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