Threshold knot selection for large-scale spatial models with applications to the Deepwater Horizon disaster

References

• Ramseur JL. Oil spills in US coastal waters: background and governance. January 2012; [cited 2016 Feb 16]. Available from: https://www.fas.org/sgp/crs/misc/RL33705.pdf.
   Google Scholar
• Goldstein BD, Osofsky HJ, Lichtveld MY. The gulf oil spill. New Engl J Med. 2011;364:1334–1348. doi:10.1056/NEJMra1007197.
   PubMed Web of Science ®Google Scholar
• McCauley LA. Environments and health: will the BP oil spill affect our health? Am J Nursing. 2010;110(9):54–56. doi:10.1097/01.NAJ.0000388266.51213.42.
   PubMed Web of Science ®Google Scholar
• Hamra GB, Guha N, Cohen A, et al. Outdoor particulate matter exposure and lung cancer: a systematic review and meta-analysis. Environ Health Perspect. 2014;122:906–911. doi:10.1289/ehp/1408092.
   PubMed Web of Science ®Google Scholar
• Lee BJ. Air pollution exposure and cardiovascular disease. Toxicol Res. 2014;30(2):71–75. doi:10.5487/TR.2014.30.2.071.
   PubMedGoogle Scholar
• Huynh T, Ramachandran G, Banerjee S, et al. Comparison of methods for analyzing left-censored occupational exposure data. Ann Occup Hyg. 2014;58(9):1126–1142. doi:10.1093/annhyg/meu067.
   PubMed Web of Science ®Google Scholar
• Quick H, Groth C, Banerjee S, et al. Exploration of the use of Bayesian modeling of gradients for censored spatiotemporal data from the Deepwater Horizon oil spill. Spat Stat. 2014;9:166–179. doi:10.1016/j.spasta.2014.03.002.
   PubMed Web of Science ®Google Scholar
• DHRC. Deepwater Horizon Research Consortia, 2016 [cited 2016 Feb 16]. Available from: http://www.niehs.nih.gov/research/supported/dert/programs/gulfconsortium/.
   Google Scholar
• Cressie N, Johannesson G. Fixed rank kriging for very large spatial data sets. J R Statist Soc Ser B. 2008;70(1):209–226. doi: 10.1111/j.1467-9868.2007.00633.x
   Google Scholar
• Banerjee S, Gelfand A, Finley A, et al. Gaussian predictive process models for large spatial datasets. J R Stat Soc Ser B. 2008;70(4):825–844. doi: 10.1111/j.1467-9868.2008.00663.x
   Google Scholar
• Cressie N, Shi T, Kang EL. Fixed rank filtering for spatio-temporal data. J Comput Graph Stat. 2010;19(3):724–745. doi: 10.1198/jcgs.2010.09051
   Web of Science ®Google Scholar
• Kang EL, Cressie N. Bayesian inference for the spatial random effects model. J Amer Statist Assoc. 2011;106(495):972–983. doi: 10.1198/jasa.2011.tm09680
   Web of Science ®Google Scholar
• Banerjee S, Finley A, Waldmann P, et al. Hierarchical spatial process models for multiple traits in large genetic trials. J Amer Statist Assoc. 2010;105(490):506–521. doi: 10.1198/jasa.2009.ap09068
   PubMed Web of Science ®Google Scholar
• Guhaniyogia R, Finley A, Banerjee S, et al. Adaptive gaussian predictive process models for large spatial datasets. Environmetrics. 2011;22:997–1007. doi: 10.1002/env.1131
   PubMed Web of Science ®Google Scholar
• Katzfuss M. Bayesian nonstationary spatial modeling for very large datasets. Environmetrics. 2013;24:189–200. doi: 10.1002/env.2200
   Web of Science ®Google Scholar
• Crainiceanu C, Diggle P, Rowlingson B. Bivariate binomial spatial modelling Loa loa prevalence in tropical africa. Johns Hopkins University Dept of Biostatistics Working Papers, Working paper 103, 2006; [cited on 2013 Nov 5]. Available from: http://biostats.bepress.com/jhubiostat/paper103.
   Google Scholar
• Nychka D, Saltzman N. Case studies in environmental statistics. In: Lecture notes in statistics, volume 132. New York: Springer-Verlag; 1998.
   Google Scholar
• Ver Hoef JM, Jansen JK. Estimating abundance from counts in large data sets of irregularly spaced plots using spatial basis functions. J Agric Biol Environ Stat. 2014 Dec;20(1):1–27. doi:10.1007/s13253-014-0192-z.
   Web of Science ®Google Scholar
• Ruppert D, Wand MP, Carroll RJ. Semiparametric regression. Cambrigde, United Kingdom: Cambridge University Press; 2003.
   Google Scholar
• Gelfand A, Banerjee S, Finley A. Spatial design for knot selection in knot-based dimension reduction models. In: Mateu J, Müller W, editors. Spatio-temporal design: advances in efficient data acquisition. Chichester, West Sussex: Wiley; 2011; 142–169.
   Google Scholar
• Madrid AE, Angulo JM, Mateu J. Spatial threshold exceedance analysis through marked point processes. Environmetrics. 2012;23(1):108–118. doi: 10.1002/env.1141
   Web of Science ®Google Scholar
• Katzfuss M, Cressie N. Tutorial on fixed rank kriging (FRK) of CO2 data. Columbus (OH): The Ohio State University; 2011a. (Department of Statistics; Technical Report 858).
   Google Scholar
• Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Statist Soc Ser B. 1977;39:1–38.
   Google Scholar
• Shi T, Cressie N. Global statistical analysis of MISR aerosol data: a massive data product from NASA's Terra satellite. Environmetrics. 2007;18:665–680. doi: 10.1002/env.864
   Web of Science ®Google Scholar
• Katzfuss M, Cressie N. Spatio-temporal smoothing and EM estimation for massive remote-sensing datasets. J Time Ser Anal. 2011b;32:430–446. doi: 10.1111/j.1467-9892.2011.00732.x
   Web of Science ®Google Scholar
• Kang EL, Cressie N, Sain SR. Combining outputs from the North American regional climate change assessment program by using a Bayesian hierarchical model. J R Statist Soc C. 2012;61(2):291–313. doi: 10.1111/j.1467-9876.2011.01010.x
   Google Scholar
• Sahu S, Challenor P. A space-time model for joint modeling of ocean temperature and salinity levels as measured by Argo floats. Environmetrics. 2008;19:509–528. doi: 10.1002/env.895
   Web of Science ®Google Scholar
• Diggle P, Lophaven S. Bayesian geostatistical design. J Scand J Statist. 2006;33:53–64. doi: 10.1111/j.1467-9469.2005.00469.x
   Web of Science ®Google Scholar
• Nychka D, Wikle C, Royle JA. Multiresolution models for nonstationary spatial covariance functions. Statist Model. 2002;2(4):315–331. doi:10.1191/1471082x02st037oa.
   Web of Science ®Google Scholar
• Wilks DS. Statistical methods in the Atmospheric sciences. 2nd ed. San Diego: Academic Press; 2006.
   Google Scholar
• Härdle W, Marron JS. Optimal bandwidth selection in nonparametric regression function estimation. Ann Statist. 1985;13(4):1465–1481. doi: 10.1214/aos/1176349748
   Web of Science ®Google Scholar
• Cressie N. Statistics for spatial data. 2nd ed. New York: Wiley; 1993.
   Google Scholar
• R. Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing; Vienna, Austria. 2018.
   Google Scholar