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2. Strong local nondeterminism of spherical fractional Brownian motion. (2018). Xiao, Yimin ; Lan, Xiaohong.
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3. Equivalence and orthogonality of Gaussian measures on spheres. (2018). Bevilacqua, Moreno ; Mateu, Jorge ; Porcu, Emilio ; Arafat, Ahmed.
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   RePEc:bla:jorssc:v:66:y:2017:i:2:p:329-344.

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1. Interpolation of daily rainfall data using censored Bayesian spatially varying model. (2020). Bakar, Shuvo K.
   In: Computational Statistics.
   RePEc:spr:compst:v:35:y:2020:i:1:d:10.1007_s00180-019-00911-0.

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2. A scalable Bayesian nonparametric model for large spatio-temporal data. (2020). Barzegar, Zahra ; Rivaz, Firoozeh.
   In: Computational Statistics.
   RePEc:spr:compst:v:35:y:2020:i:1:d:10.1007_s00180-019-00905-y.

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3. An area-specific stick breaking process for spatial data. (2019). Khaledi, Majid Jafari ; Hosseinpouri, Mahdi.
   In: Statistical Papers.
   RePEc:spr:stpapr:v:60:y:2019:i:1:d:10.1007_s00362-016-0833-0.

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4. Spatiotemporal Lagged Models for Variable Rate Irrigation in Agriculture. (2019). Pugh, Sierra ; Hansen, Neil ; Heaton, Matthew J ; Svedin, Jeff.
   In: Journal of Agricultural, Biological and Environmental Statistics.
   RePEc:spr:jagbes:v:24:y:2019:i:4:d:10.1007_s13253-019-00365-3.

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5. Bayesian nonstationary Gaussian process models via treed process convolutions. (2019). , Waley.
   In: Advances in Data Analysis and Classification.
   RePEc:spr:advdac:v:13:y:2019:i:3:d:10.1007_s11634-018-0341-2.

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6. Multivariate spatial meta kriging. (2019). Banerjee, Sudipto ; Guhaniyogi, Rajarshi.
   In: Statistics & Probability Letters.
   RePEc:eee:stapro:v:144:y:2019:i:c:p:3-8.

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7. Producing high-quality solar resource maps by integrating high- and low-accuracy measurements using Gaussian processes. (2019). Yang, Dazhi ; Gueymard, Christian A.
   In: Renewable and Sustainable Energy Reviews.
   RePEc:eee:rensus:v:113:y:2019:i:c:45.

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8. Bayesian sequential data collection for stochastic simulation calibration. (2019). Xie, Wei ; Zhang, Qiong ; Wang, BO.
   In: European Journal of Operational Research.
   RePEc:eee:ejores:v:277:y:2019:i:1:p:300-316.

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9. Likelihood approximation with hierarchical matrices for large spatial datasets. (2019). Sun, Ying ; Genton, Marc G ; Keyes, David E ; Litvinenko, Alexander.
   In: Computational Statistics & Data Analysis.
   RePEc:eee:csdana:v:137:y:2019:i:c:p:115-132.

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10. Comments on: Process modeling for slope and aspect with application to elevation data maps. (2018). Schliep, Erin M.
    In: TEST: An Official Journal of the Spanish Society of Statistics and Operations Research.
    RePEc:spr:testjl:v:27:y:2018:i:4:d:10.1007_s11749-018-0620-4.

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11. Two-scale spatial models for binary data. (2018). Cressie, Noel ; Hardouin, Cecile.
    In: Statistical Methods & Applications.
    RePEc:spr:stmapp:v:27:y:2018:i:1:d:10.1007_s10260-017-0391-1.

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12. Reduced Basis Kriging for Big Spatial Fields. (2018). Sain, Stephan ; Maitra, Ranjan ; Pazdernik, Karl ; Nychka, Douglas.
    In: Sankhya A: The Indian Journal of Statistics.
    RePEc:spr:sankha:v:80:y:2018:i:2:d:10.1007_s13171-018-0129-7.

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13. Estimation of spatial autoregressive models with measurement error for large data sets. (2018). Suesse, Thomas.
    In: Computational Statistics.
    RePEc:spr:compst:v:33:y:2018:i:4:d:10.1007_s00180-017-0774-7.

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14. Principles for statistical inference on big spatio-temporal data from climate models. (2018). Castruccio, Stefano ; Genton, Marc G.
    In: Statistics & Probability Letters.
    RePEc:eee:stapro:v:136:y:2018:i:c:p:92-96.

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15. Big data sampling and spatial analysis: “which of the two ladles, of fig-wood or gold, is appropriate to the soup and the pot?”. (2018). Bivand, Roger ; Krivoruchko, Konstantin.
    In: Statistics & Probability Letters.
    RePEc:eee:stapro:v:136:y:2018:i:c:p:87-91.

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16. A sparse linear algebra algorithm for fast computation of prediction variances with Gaussian Markov random fields. (2018). Rougier, Jonathan ; Zammit-Mangion, Andrew.
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:123:y:2018:i:c:p:116-130.

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17. Spatial data compression via adaptive dispersion clustering. (2018). Marchetti, Yuliya ; Cressie, Noel ; Nguyen, Hai ; Braverman, Amy.
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:117:y:2018:i:c:p:138-153.

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18. A scalable multi‐resolution spatio‐temporal model for brain activation and connectivity in fMRI data. (2018). Castruccio, Stefano ; Genton, Marc G ; Ombao, Hernando.
    In: Biometrics.
    RePEc:bla:biomet:v:74:y:2018:i:3:p:823-833.

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19. Tukey -and- Random Fields. (2017). Genton, Marc G ; Xu, Ganggang.
    In: Journal of the American Statistical Association.
    RePEc:taf:jnlasa:v:112:y:2017:i:519:p:1236-1249.

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20. A Multi-Resolution Approximation for Massive Spatial Datasets. (2017). Katzfuss, Matthias.
    In: Journal of the American Statistical Association.
    RePEc:taf:jnlasa:v:112:y:2017:i:517:p:201-214.

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21. Gaussian processes for computer experiments. (2017). Rulliere, Didier ; Contal, Emile ; Maatouk, Hassan ; Bachoc, Franois.
    In: Post-Print.
    RePEc:hal:journl:hal-01665936.

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22. Regionalization of multiscale spatial processes by using a criterion for spatial aggregation error. (2017). Holan, Scott H ; Bradley, Jonathan R ; Wikle, Christopher K.
    In: Journal of the Royal Statistical Society Series B.
    RePEc:bla:jorssb:v:79:y:2017:i:3:p:815-832.

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23. Efficient Bayesian hierarchical functional data analysis with basis function approximations using Gaussian–Wishart processes. (2017). Choi, Taeryon ; Ren, Peng ; Lee, Jong Soo ; Yang, Jingjing ; Cox, Dennis D.
    In: Biometrics.
    RePEc:bla:biomet:v:73:y:2017:i:4:p:1082-1091.

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24. Modeling nonstationarity in space and time. (2017). Shand, Lyndsay ; Li, BO.
    In: Biometrics.
    RePEc:bla:biomet:v:73:y:2017:i:3:p:759-768.

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25. A non-homogeneous skew-Gaussian Bayesian spatial model. (2016). Khaledi, Majid ; Boojari, Hossein ; Rivaz, Firoozeh.
    In: Statistical Methods & Applications.
    RePEc:spr:stmapp:v:25:y:2016:i:1:p:55-73.

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26. Variable Family Size Based Spatial Moving Correlations Model. (2016). Mariathas, Hensley H ; Sutradhar, Brajendra C.
    In: Sankhya B: The Indian Journal of Statistics.
    RePEc:spr:sankhb:v:78:y:2016:i:1:d:10.1007_s13571-015-0104-4.

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27. A Fused Lasso Approach to Nonstationary Spatial Covariance Estimation. (2016). Parker, Ryan J ; Eidsvik, JO ; Reich, Brian J.
    In: Journal of Agricultural, Biological and Environmental Statistics.
    RePEc:spr:jagbes:v:21:y:2016:i:3:d:10.1007_s13253-016-0251-8.

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28. A geostatistical approach to the change-of-support problem and variable-support data fusion in spatial analysis. (2016). Wang, Yang ; Zeng, Hui.
    In: Journal of Geographical Systems.
    RePEc:kap:jgeosy:v:18:y:2016:i:1:d:10.1007_s10109-015-0224-4.

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29. laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R. (2016). Gramacy, Robert B.
    In: Journal of Statistical Software.
    RePEc:jss:jstsof:v:072:i01.

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30. Asymptotic properties of multivariate tapering for estimation and prediction. (2016). Furrer, Reinhard ; Du, Juan ; Bachoc, Franois.
    In: Journal of Multivariate Analysis.
    RePEc:eee:jmvana:v:149:y:2016:i:c:p:177-191.

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31. Modeling nonstationary covariance function with convolution on sphere. (2016). Zhu, Zhengyuan ; Li, Yang.
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:104:y:2016:i:c:p:233-246.

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32. Rejoinder on: Comparing and selecting spatial predictors using local criteria. (2015). Shi, Tao ; Cressie, Noel ; Bradley, Jonathan.
    In: TEST: An Official Journal of the Spanish Society of Statistics and Operations Research.
    RePEc:spr:testjl:v:24:y:2015:i:1:p:54-60.

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33. Comparing and selecting spatial predictors using local criteria. (2015). Shi, Tao ; Cressie, Noel ; Bradley, Jonathan.
    In: TEST: An Official Journal of the Spanish Society of Statistics and Operations Research.
    RePEc:spr:testjl:v:24:y:2015:i:1:p:1-28.

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34. The SAR Model for Very Large Datasets: A Reduced Rank Approach. (2015). Burden, Sandy ; Cressie, Noel ; Steel, David G..
    In: Econometrics.
    RePEc:gam:jecnmx:v:3:y:2015:i:2:p:317-338:d:49389.

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35. Numerical Methods of Karhunen–Loève Expansion for Spatial Data. (2015). Hao, Zhang ; Juan, Hu.
    In: Stochastics and Quality Control.
    RePEc:bpj:ecqcon:v:30:y:2015:i:1:p:49-58:n:5.

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36. Fast Prediction of Deterministic Functions Using Sparse Grid Experimental Designs. (2014). Plumlee, Matthew.
    In: Journal of the American Statistical Association.
    RePEc:taf:jnlasa:v:109:y:2014:i:508:p:1581-1591.

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37. A pseudo-penalized quasi-likelihood approach to the spatial misalignment problem with non-normal data. (2014). Young, Linda J. ; Lopiano, Kenneth K. ; Gotway, Carol A..
    In: Biometrics.
    RePEc:bla:biomet:v:70:y:2014:i:3:p:648-660.

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38. Discussing the “big n problem”. (2013). Lasinio, Giovanna Jona ; Mastrantonio, Gianluca ; Pollice, Alessio.
    In: Statistical Methods & Applications.
    RePEc:spr:stmapp:v:22:y:2013:i:1:p:97-112.

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39. Efficient Approximation of the Spatial Covariance Function for Large Datasets - Analysis of Atmospheric CO2 Concentrations. (2013). Schwarze, Reimund ; Vetter, Patrick ; Schmid, Wolfgang.
    In: Discussion Paper Series RECAP15.
    RePEc:euv:dpaper:009.

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40. Spatial prediction of traffic levels in unmeasured locations: applications of universal kriging and geographically weighted regression. (2013). Kockelman, Kara M ; Selby, Brent .
    In: Journal of Transport Geography.
    RePEc:eee:jotrge:v:29:y:2013:i:c:p:24-32.

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41. A comparison between Markov approximations and other methods for large spatial data sets. (2013). Lindgren, Finn ; Bolin, David.
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:61:y:2013:i:c:p:7-21.

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42. Hierarchical Factor Models for Large Spatially Misaligned Data: A Low-Rank Predictive Process Approach. (2013). Banerjee, Sudipto ; Ren, Qian.
    In: Biometrics.
    RePEc:bla:biomet:v:69:y:2013:i:1:p:19-30.

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43. Bayesian dynamic modeling for large space-time datasets using Gaussian predictive processes. (2012). Banerjee, Sudipto ; Finley, Andrew ; Gelfand, Alan.
    In: Journal of Geographical Systems.
    RePEc:kap:jgeosy:v:14:y:2012:i:1:p:29-47.

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44. A simplified representation of the covariance structure of axially symmetric processes on the sphere. (2012). Robeson, Scott M. ; Zhang, Haimeng ; Huang, Chunfeng.
    In: Statistics & Probability Letters.
    RePEc:eee:stapro:v:82:y:2012:i:7:p:1346-1351.

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45. Small area estimation under spatial nonstationarity. (2012). Chambers, Ray ; Tzavidis, Nikos ; Salvati, Nicola ; Chandra, Hukum.
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:56:y:2012:i:10:p:2875-2888.

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46. A Class of Convolution‐Based Models for Spatio‐Temporal Processes with Non‐Separable Covariance Structure. (2010). Diggle, Peter J. ; Rodrigues, Alexandre.
    In: Scandinavian Journal of Statistics.
    RePEc:bla:scjsta:v:37:y:2010:i:4:p:553-567.

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47. Spatiotemporal smoothing and sulphur dioxide trends over Europe. (2009). Scott, Marian E. ; Giannitrapani, Marco ; Bowman, Adrian W..
    In: Journal of the Royal Statistical Society Series C.
    RePEc:bla:jorssc:v:58:y:2009:i:5:p:737-752.

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48. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. (2009). Chopin, Nicolas ; Martino, Sara ; Håvard Rue, .
    In: Journal of the Royal Statistical Society Series B.
    RePEc:bla:jorssb:v:71:y:2009:i:2:p:319-392.

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49. Hierarchical Spatial Modeling of Additive and Dominance Genetic Variance for Large Spatial Trial Datasets. (2009). Ericsson, Tore ; Banerjee, Sudipto ; Waldmann, Patrik ; Finley, Andrew O..
    In: Biometrics.
    RePEc:bla:biomet:v:65:y:2009:i:2:p:441-451.

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50. Gaussian predictive process models for large spatial data sets. (2008). Banerjee, Sudipto ; Sang, Huiyan ; Finley, Andrew O. ; Gelfand, Alan E..
    In: Journal of the Royal Statistical Society Series B.
    RePEc:bla:jorssb:v:70:y:2008:i:4:p:825-848.

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