A spatial causal analysis of wildland fire-contributed PM2.5 using numerical model output-Reference-Cited by-同舟云学术

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A spatial causal analysis of wildland fire-contributed PM2.5 using numerical model output

Published:2022-12-01 Issue:4 Volume:16 Page:
ISSN:1932-6157
Container-title:The Annals of Applied Statistics
language:
Short-container-title:Ann. Appl. Stat.

Author:

Larsen Alexandra¹,Yang Shu²,Reich Brian J.²,Rappold Ana G.³

Affiliation:

1. Department of Biostatistics and Bioinformatics, Duke University

2. Department of Statistics, North Carolina State University

3. National Health and Environmental Effects Research Laboratory—Environmental Public Health Division, US Environmental Protection Agency

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Reference49 articles.

1. Chilès, J.-P. and Delfiner, P. (2012). Geostatistics: Modeling Spatial Uncertainty, 2nd ed. Wiley Series in Probability and Statistics. Wiley, Hoboken, NJ.

2. ALLEN, M. R. and STOTT, P. A. (2003). Estimating signal amplitudes in optimal fingerprinting, Part I: Theory. Clim. Dyn. 21 477–491.

3. BENMAP (2017). Environmental Benefits Mapping and Analysis Program—Community Edition User’s Manual Appendices Technical Report.

4. BERROCAL, V. J., GELFAND, A. E. and HOLLAND, D. M. (2010). A spatio-temporal downscaler for output from numerical models. J. Agric. Biol. Environ. Stat. 15 176–197.

5. CRESSIE, N. A. C. (1993). Statistics for Spatial Data. Wiley Series in Probability and Statistics. John Wiley & Sons, Inc., Hoboken, NJ, USA.

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Daily Local-Level Estimates of Ambient Wildfire Smoke PM_2.5 for the Contiguous US;Environmental Science & Technology;2022-09-22

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