Webbläsaren som du använder stöds inte av denna webbplats. Alla versioner av Internet Explorer stöds inte längre, av oss eller Microsoft (läs mer här: * https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Var god och använd en modern webbläsare för att ta del av denna webbplats, som t.ex. nyaste versioner av Edge, Chrome, Firefox eller Safari osv.

Reduced-rank spatio-temporal modeling of air pollution concentrations in the Multi-Ethnic Study of Atherosclerosis and Air Pollution

Författare

  • Casey Olives
  • Lianne Sheppard
  • Johan Lindström
  • Paul D Sampson
  • Joel D Kaufman
  • Adam A Szpiro

Summary, in English

There is growing evidence in the epidemiologic literature of the relationship between air pollution and adverse health outcomes. Prediction of individual air pollution exposure in the Environmental Protection Agency (EPA) funded Multi-Ethnic Study of Atheroscelerosis and Air Pollution (MESA Air) study relies on a exible spatio-temporal prediction model that integrates land-use regression with kriging to account for spatial dependence in pollutant concentrations. Temporal variability is captured using temporal trends estimated via modified singular value decomposition and temporally varying spatial residuals. This model utilizes monitoring data from existing regulatory networks and supplementary MESA Air monitoring data to predict concentrations for individual cohort members.



In general, spatio-temporal models are limited in their efficacy for large datasets due to computational intractability. We develop reduced-rank versions of the MESA Air spatio-temporal model. To do so, we apply low-rank kriging to account for spatial variation in the mean process and discuss the limitations of this approach. As an alternative, we represent spatial variation using thin plate regression splines. We compare the performance of the outlined models using EPA and MESA Air monitoring data for predicting concentrations of

oxides of nitrogen (NOx) - a pollutant of primary interest in MESA Air - in the Los Angeles metropolitan area via cross-validated R2.



Our findings suggest that use of reduced-rank models can improve computational eciency in certain cases. Low-rank kriging and thin plate regression splines were competitive across the formulations considered, although TPRS appeared to be more robust in some settings.

Publiceringsår

2014

Språk

Engelska

Sidor

2509-2537

Publikation/Tidskrift/Serie

Annals of Applied Statistics

Volym

8

Issue

4

Dokumenttyp

Artikel i tidskrift

Förlag

Institute of Mathematical Statistics

Ämne

  • Probability Theory and Statistics

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1932-6157