Every few days, we will be publishing layman’s abstracts of new articles from our prestigious portfolio of journals in statistics. The aim is to highlight the latest research to a broader audience in an accessible format.
Environmental air pollution like PM10 concentration as a “spatial data” encompasses various particulate matters. The increased ambient particulate matter from industrialization and urbanization is highly associated with morbidity and mortality worldwide, presenting one of the most severe environmental pollution problems. Spatial data is information about a physical object that can be represented by numerical values in a geographic coordinate system. These data represent the location, size, and shape of an object on planet Earth such as a building, lake, mountain or township. On the other hand, an outlier is an observation that lies an abnormal distance from other values in a random sample from a population. Outliers should be investigated carefully. Often they contain valuable information about the process under investigation or the data gathering and recording process. In spatial data modeling, it is also commonly assumed that the covariates are observed without errors, but for various reasons such as measurement techniques or instruments used, uncertainty is inherent in spatial (especially geostatistics) data and so these data are susceptible to measurement error in the covariates of interest. In this study, wind speed, which is a measurement error covariate, is used to predict PM10. To that end, statistical inference for such data (spatial data with outlier and covariate measurement error) is carried out in a likelihood approach. The proposed approach is evaluated through a simulation study and also by a real application (particulate matters dataset).
The full article can be read online here.
Copyright: Patrick Rhodes