Static and roving sensor data fusion for spatio-temporal hazard mapping with application to occupational exposure assessment

DOI10.1214/16-AOAS995zbMath1366.62259WikidataQ57136691 ScholiaQ57136691MaRDI QIDQ2628529

Tingjin Chu, Jun Zhu, Kirsten Koehler, Guilherme Ludwig, Haonan Wang

Publication date: 2 June 2017

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aoas/1491616875

zbMATH Keywords

krigingspatial statisticsgeostatisticssemiparametric methodsspatio-temporal statisticshazard assessment

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12) Nonparametric estimation (62G05) Point estimation (62F10) Geostatistics (86A32)

Related Items (2)

Semiparametric method and theory for continuously indexed spatio-temporal processes ⋮ Spatio-temporal expanding distance asymptotic framework for locally stationary processes

This page was built for publication: Static and roving sensor data fusion for spatio-temporal hazard mapping with application to occupational exposure assessment