Interpretable, predictive spatio-temporal models via enhanced pairwise directions estimation

References

• K.S. Bakar and S.K. Sahu, sptimer: spatio-temporal bayesian modelling using R, J. Stat. Softw. 63 (2015), pp. 1–32.
   Web of Science ®Google Scholar
• S. Banerjee, A.E. Gelfand, A.O. Finley, and H. Sang, Gaussian predictive process models for large spatial data sets, J. R. Stat. Soc. Series B. Stat. Methodol. 70 (2008), pp. 825–848.
   PubMed Web of Science ®Google Scholar
• I. Braud and C. Obled, On the use of empirical orthogonal function (EOF) analysis in the simulation of random fields, Stoch. Hydrol. Hydraul. 5 (1991), pp. 125–134.
   Google Scholar
• R. Coudret, S. Girard, and J. Saracco, A new sliced inverse regression method for multivariate response, Comput. Stat. Data. Anal. 77 (2014), pp. 285–299.
   Web of Science ®Google Scholar
• N. Cressie, Statistics for Spatial Data, John Wiley & Sons, Hoboken, 2015.
   Google Scholar
• N. Cressie and G. Johannesson, Fixed rank kriging for very large spatial data sets, J. R. Stat. Soc. Series B. Stat. Methodol. 70 (2008), pp. 209–226.
   Web of Science ®Google Scholar
• N. Cressie and C.K. Wikle, Statistics for Spatio-Temporal Data, John Wiley & Sons, Boca Raton, 2011.
   Google Scholar
• N. Cressie, T. Shi, and E.L. Kang, Fixed rank filtering for spatio-temporal data, J. Comput. Graph. Stat. 19 (2010), pp. 724–745.
   Web of Science ®Google Scholar
• U. Demšar, P. Harris, C. Brunsdon, A.S. Fotheringham, and S. McLoone, Principal component analysis on spatial data: an overview, Ann. Assoc. Am. Geogr. 103 (2013), pp. 106–128.
   Web of Science ®Google Scholar
• A. Fassò and M. Cameletti, A unified statistical approach for simulation, modeling, analysis and mapping of environmental data, Simulation 86 (2010), pp. 139–153.
   Google Scholar
• R. Giraldo, P. Delicado, and J. Mateu, geofd: Spatial Prediction for Function Value Data (2020). Available at https://CRAN.R-project.org/package=geofd, R package version 2.0.
   Google Scholar
• T. Gneiting and A.E. Raftery, Strictly proper scoring rules, prediction, and estimation, J. Am. Stat. Assoc. 102 (2007), pp. 359–378.
   Web of Science ®Google Scholar
• O. Gromenko, P. Kokoszka, L. Zhu, and J. Sojka, Estimation and testing for spatially indexed curves with application to ionospheric and magnetic field trends, Ann. Appl. Stat. 6 (2012), pp. 669–696.
   Web of Science ®Google Scholar
• R. Ignaccolo, J. Mateu, and R. Giraldo, Kriging with external drift for functional data for air quality monitoring, Stoch. Environ. Res. Risk. Assess. 28 (2014), pp. 1171–1186.
   Web of Science ®Google Scholar
• P. Kokoszka and M. Reimherr, Some recent developments in inference for geostatistical functional data, Rev. Colomb. Estad. 42 (2019), pp. 101–122.
   Google Scholar
• D.J. Lee and M. Durbán, P-spline anova-type interaction models for spatio-temporal smoothing, Stat. Model. 11 (2011), pp. 49–69.
   Web of Science ®Google Scholar
• K.C. Li, Y. Aragon, K. Shedden, and C. Thomas Agnan, Dimension reduction for multivariate response data, J. Am. Stat. Assoc. 98 (2003), pp. 99–109.
   Web of Science ®Google Scholar
• H.H. Lue, Sliced inverse regression for multivariate response regression, J. Stat. Plan. Inference 139 (2009), pp. 2656–2664.
   Web of Science ®Google Scholar
• H.H. Lue, On principal hessian directions for multivariate response regressions, Comput. Stat. 25 (2010), pp. 619–632.
   Web of Science ®Google Scholar
• H.H. Lue, Pairwise directions estimation for multivariate response regression data, J. Stat. Comput. Simul. 89 (2019), pp. 776–794.
   Web of Science ®Google Scholar
• R. Luo, H. Wang, and C.L. Tsai, Contour projected dimension reduction, Ann. Stat. 37 (2009), pp. 3743–3778.
   Web of Science ®Google Scholar
• J.D. Martin and T.W. Simpson, Use of kriging models to approximate deterministic computer models, AIAA J. 43 (2005), pp. 853–863.
   Google Scholar
• E. Pebesma and G. Heuvelink, Spatio-temporal interpolation using gstat, R J. 8 (2016), pp. 204–218.
   Google Scholar
• J. Sharples and M. Hutchinson, Spatio-temporal analysis of climatic data using additive regression splines, in Proceedings of International Congress on Modelling and Simulation (MODSIM' 05). Citeseer, 2005, pp. 1695–1701.
   Google Scholar
• M. Sherman, Spatial Statistics and Spatio-temporal Data: covariance functions and directional properties, John Wiley & Sons, Chichester, 2011.
   Google Scholar
• J.T. Thorson, W. Cheng, A.J. Hermann, J.N. Ianelli, M.A. Litzow, C.A. O'Leary, and G.G. Thompson, Empirical orthogonal function regression: linking population biology to spatial varying environmental conditions using climate projections, Glob. Chang. Biol. 26 (2020), pp. 4638–4649.
   PubMed Web of Science ®Google Scholar
• S. Tzeng and H.C. Huang, Resolution adaptive fixed rank kriging, Technometrics 60 (2018), pp. 198–208.
   Web of Science ®Google Scholar
• W.T. Wang and H.C. Huang, Regularized principal component analysis for spatial data, J. Comput. Graph. Stat. 26 (2017), pp. 14–25.
   Web of Science ®Google Scholar
• C.K. Wikle, Low-rank representations for spatial processes, Handb. Spat. Stat. 107 (2010), pp. 118.
   Google Scholar
• S.N. Wood, Generalized Additive Models: an introduction with R, CRC press, Boca Raton, 2017.
   Google Scholar
• Y. Xia, H. Tong, W.K. Li, and L.X. Zhu, An adaptive estimation of dimension reduction space, J. R. Stat. Soc. Series B. Stat. Methodol. 64 (2002), pp. 363–410.
   Web of Science ®Google Scholar
• K. Yang and P. Qiu, Nonparametric estimation of the spatio-temporal covariance structure, Stat. Med. 38 (2019), pp. 4555–4565.
   PubMed Web of Science ®Google Scholar