Načítá se...
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...
Uloženo v:
| Vydáno v: | Environmetrics |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Artigo |
| Jazyk: | Inglês |
| Vydáno: |
2019
|
| Témata: | |
| On-line přístup: | 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 |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo otaguje tento záznam!
|