Multivariate Air Pollution Prediction Modeling with partial Missingness.

ABSTRACT: Missing observations from air pollution monitoring networks have posed a longstanding problem for health investigators of air pollution. Growing interest in mixtures of air pollutants has further complicated this problem, as many new challenges have arisen that require development of novel methods. The objective of this study is to develop a methodology for multivariate prediction of air pollution. We focus specifically on tackling different forms of missing data, such as: spatial (sparse sites), outcome (pollutants not measured at some sites), and temporal (varieties of interrupted time series). To address these challenges, we develop a novel multivariate fusion framework, which leverages the observed inter-pollutant correlation structure to reduce error in the simultaneous prediction of

SUBMITTER: Boaz RM 

PROVIDER: S-EPMC6980235 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature