Spatial Statistics

18 Oct 2018 20:23

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That is, statistics for random variables spread out in space. Includes statistical image processing, which is important but doesn't really grab me as an application.

The case where the random variables also evolve in time, the spatio-temporal case, is interesting enough to get its own notebook.

See also: Markov Models; Network Data Analysis Pattern Formation; Point Processes; Random Fields; Statistics

Recommended, general:
• Carlo Gaetan and Xavier Guyon, Spatial Statistics and Modeling [Mini-review]
• Peter Guttorp, Stochastic Modeling of Scientific Data
• Brian D. Ripley, Statistical Inference for Spatial Processes

Recommended, of more specialized interest:
• A. C. Davison, S. A. Padoan and M. Ribatet, "Statistical Modeling of Spatial Extremes", Statistical Science 27 (2012): 161--186 [with discussion and response]
• Florence Forbes, Myriam Charras-Garrido, Lamiae Azizi, Senan Doyle, and David Abrial, "Spatial risk mapping for rare disease with hidden Markov fields and variational EM", Annals of Applied Statistics 7 (2013): 1992--1216
• M. Ghosh and J. N. K. Rao, "Small Area Estimation: An Appraisal", Statistical Science 9 (1994): 55--76 [Plus discussions and reply]
• Xavier Guyon, Random Fields on a Network
• Karen Kafadar, "Smoothing Geographical Data, Particularly Rates of Disease", Statistics in Medicine 15 (1996): 2539--2560 [PDF reprint via Prof. Kafadar]
• Gary King, A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data [Review]
• S. N. Lahiri, Resampling Methods for Dependent Data [Mini-review]
• Elizaveta Levina and Peter J. Bickel, "Texture synthesis and nonparametric resampling of random fields", Annals of Statistics 34 (2006): 1751--1773
• John Novembre and Matthew Stephens, "Interpreting principal component analyses of spatial population genetic variation", Nature Genetics 40 (2008): 646--649 [Many PCA patterns commonly taken to be signs of ancestral population movements can also be produced as artifacts from null models. This is distressing, since many of the results based on PCA maps are things which make sense and I'd like to be true, but Novembre and Stephens's arguments check out.]
• R. Piasecki, M. T. Martin, and A. Plastino, "Inhomogeneity and complexity measures for spatial patterns," cond-mat/0107471
• Peter I. Saparin, Wolfgang Gowin, Jürgen Kurths, and Dieter Felsenber, "Quantification of cancellous bone structure using symbolic dynamics and measures of complexity", Physical Review E 58 (1998): 6449--6459
• Grace Wahba, Spline Models for Observational Data
• Michael E. Wall, Andreas Rechtsteiner and Luis M. Rocha, "Singular Value Decomposition and Principal Component Analysis," physics/0208101
• Rongjing Xiang and Jennifer Neville, "Relational Learning with One Network: An Asymptotic Analysis", AI Stats 2011 [PDF reprint]

Modesty forbids me to recommend:
• CRS, lecture notes for 36-467, Data over Space and Time

To read:
• Stéphanie Allassonniere, Estelle Kuhn, "Convergent Stochastic Expectation Maximization algorithm with efficient sampling in high dimension. Application to deformable template model estimation", arxiv:1207.5938
• Renato M. Assuncao and Pablo A. Ferrari, "Detection of spatial pattern through independence of thinned processes," math.PR/0103104
• Yves F. Atchade, "Estimation of Network structures from partially observed Markov random fields", arxiv:1108.2835
• Claus Beisbart, Thomas Buchert and Herbert Wagner, "Morphometry of Spatial Patterns," astro-ph/0007459
• Claus Beisbart, Martin Kerscher and Klaus Mecke, "Mark correlations: relating physical properties to spatial distributions," physics/0201069
• Claus Beisbart, Robert Dahlke, Klaus Mecke, and Herbert Wagner, "Vector- and tensor-valued descriptors for spatial patterns," physics/0203072
• Roger S. Bivand, Applied Spatial Data Analysis with R
• A. Brezger, L. Fahrmeir, A. Hennerfeind, "Adaptive Gaussian Markov random fields with applications in human brain mapping", Journal of the Royal Statistical Society C 56 (2007): 327--345
• Chris Brunsdon and Lex Comber, An Introduction to R for Spatial Analysis and Mapping
• E. Ceyhan, C. E. Priebe, D. J. Marchette, "A New Family of Random Graphs for Testing Spatial Segregation", Canadian Journal of Statistics 35 (2007): 27--50, arxiv:0802.0615
• David B. Chua, Eric D. Kolaczyk, and Mark Crovella, "Network Kriging", math.ST/0510013
• Cressie, Statistics for Spatial Data
• S. Dachian, "Nonparametric estimation for Gibbs random fields specified through one-point systems", Statistical Inference for Stochastic Processes 1 (1998): 245--264
• Jorn Davidsen, Peter Grassberger and Maya Paczuski, "Networks of Recurrent Events, a Theory of Records, and an Application to Finding Causal Signatures in Seismicity", physics/0701190
• Tilman M. Davies, Martin L. Hazelton, Jonathan. C Marshall, "sparr: Analyzing Spatial Relative Risk Using Fixed and Adaptive Kernel Density Estimation in R", Journal of Statistical Software 39:1 (2011)
• Peter J. Diggle, Statistical Analysis of Spatial and Spatio-Temporal Point Patterns
• Mohamed El Machkouri, "Asymptotic normality of the Parzen-Rosenblatt density estimator for strongly mixing random fields", arxiv:1008.1342
• Samuel Elogne and Dionisis Hristopulos, "On the Inference of Spartan Spatial Random Field Models for Geostatistical Applications", math.ST/0603430
• Bryan K. Epperson, Geographical Genetics
• Jacob Feldman and Manish Singh, "Bayesian estimation of the shape skeleton", Proceedings of the National Academy of Sciences (USA) 103 (2006): 18014--18019 [From the abstract, it sounds like this could really have been "penalized maximum likelihood estimation of the shape skeleton", since they're just doing MAP rather than any kind of averaging, or otherwise working with the posterior distribution.]
• C. Fonseca, H. Ferreira, L. Pereira A.P., Martins, "Stability and contagion measures for spatial extreme value analyses", arxiv:1206.1228
• Florence Forbes and Nathalie Peyrard, "Hidden Markov Random Field Model Selection Criteria Based on Mean Field-Like Approximations", IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (2003): 1089--1101 [PostScript preprint]
• Marie-Josie Fortin and Mark R. Dale, Spatial Analysis: A Guide for Ecologists
• Michael Friendly, "A.-M. Guerry's Moral Statistics of France: Challenges for Multivariable Spatial Analysis", Statistical Science 22 (2007): 368--399, arxiv:0801.4263
• Alan E. Gelfand, Peter J. Diggle, Montserrat Fuentes and Peter Guttorp (eds.), Handbook of Spatial Statistics
• Anandamohan Ghosh, V. Ravi Kumar and B. D. Kulkarni, "Parameter estimation in spatially extended systems: The Karhunen-Loeve and Galerkin multiple shooting approach," nlin.CD/0112029 = Physical Review E 64 (2001): 056222
• Tilmann Gneiting, Hana Ševčíková, and Donald B. Percival, "Estimators of Fractal Dimension: Assessing the Roughness of Time Series and Spatial Data", Statistical Science 27 (2012): 247--277
• Priscilla E. Greenwood and Wolfgang Wefelmeyer, "Characterizing Efficient Empirical Estimators for Local Interaction Gibbs Fields", Statistical Inference for Stochastic Processes 2 (1999): 119--134
• Aude Grelaud, Christian Robert, Jean-Michel Marin, Francois Rodolphe, Jean-Francois Taly, "ABC likelihood-freee methods for model choice in Gibbs random fields", arxiv:0807.2767
• Zachary T. Harmany, Roummel F. Marcia, Rebecca M. Willett, "This is SPIRAL-TAP: Sparse Poisson Intensity Reconstruction ALgorithms --- Theory and Practice", IEEE Transactions on Image Processing 21 (2012): 1084--1096, arxiv:1005.4274
• D. T. Hristopulos and S. N. Elogne, "Fast Spatial Prediction from Inhomogeneously Sampled Data Based on Generalized Random Fields with Gibbs Energy Functionals", physics/0609071
• John Hughes, Murali Haran, "Dimension reduction and alleviation of confounding for spatial generalized linear mixed models", Journal of the Royal Statistical Society B 75 (2013): 139--159
• Jun-ichi Inoue and Kazuyuki Tanaka, "Dynamics of the Maximum Marginal Likelihood Hyper-parameter Estimation in Image Restoration: Gradient Descent vs. EM Algorithm," cond-mat/0107023
• Mark S. Kaiser, Soumendra N. Lahiri, and Daniel J. Nordman, "Goodness of fit tests for a class of Markov random field models", Annals of Statistics 40 (2012): 104--130, arxiv:1205.6086
• Wolfgang Karcher, Elena Shmileva, Evgeny Spodarev, "Extrapolation of stable random fields", arxiv:1107.1654
• D. G. Krige, Lognormal-de Wijsian Geostatistics for Ore Evaluation (Johannesburg: South African Institute of Mining and Metallurgy, 1981)
• Nhu D. Le and James V. Zidek, Statistical Analysis of Environmental Space-Time Processes
• U. K. Lee, H. Choi, B. U. Park and K. S. Yu, "Local likelihood density estimation on random fields", Statistics and Probability Letters 68 (2004): 347--357
• Loic Le Gratiet and Claire Cannamela, "Kriging-based sequential design strategies using fast cross-validation techniques with extensions to multi-fidelity computer codes", arxiv:1210.6187
• Pei-Sheng Lin and Murray K. Clayton, "Analysis of binary spatial data by quasi-likelihood estimating equations", math.ST/0505602 = Annals of Statistics 33 (2005): 542--555
• Zudi Lu and Xing Chen, "Spatial kernel regression estimation: weak consistency", Statistics and Probability Letters 68 (2004): 125--136
• Jorge Mateu and Francisco Montes, "Pseudo-likelihood Inference for Gibbs Processes with Exponential Families through Generalized Linear Models", Statistical Inference for Stochastic Processes 4 (2001): 125--154
• Klaus R. Mecke and D. Stoyan (eds.)
  • Statistical Physics and Spatial Statistics: The Art of Analyzing and Modeling Spatial Structures and Pattern Formation
  • Morphology of Condensed Matter: Physics and Geometry of Spatially Complex Systems
• Jose-Maria Montero, Gema Fernandez-Aviles and Jorge Mateu, Spatial and Spatio-Temporal Geostatistical Modeling and Kriging
• Werner G. Muller, Collecting Spatial Data: Optimum Design of Experiments for Random Fields
• Enza Orlandi, Eva Loecherbach, "On the neighborhood radius estimation in Variable-neighborhood Markov Random Fields", arxiv:1002.4850
• Christopher J. Paciorek, "Spatial models for point and areal data using Markov random fields on a fine grid", Electronic Journal of Statistics 7 (2013): 946--972
• Marcelo Pereyra, Nicolas Dobigeon, Hadj Batatia, Jean-Yves Tourneret, "Computing the Cramer-Rao bound of Markov random field parameters: Application to the Ising and the Potts models", arxiv:1206.3985
• Xin Qi and Hongyu Zhao, "Asymptotic efficiency and finite-sample properties of the generalized profiling estimation of parameters in ordinary differential equations", Annals of Statistics 38 (2010): 435--481
• Laura M. Sangalli, James O. Ramsay, Timothy O. Ramsay, "Spatial spline regression models", Journal of the Royal Statistical Society B 75 (2013): 681--703
• Peter St. Jean, Pockets of Crime: Broken Windows, Collective Efficacy, and the Criminal Point of View [blurb]
• Jeffrey E. Steif, "Consistent estimation of joint distributions for sufficiently mixing random fields", Annals of Statistics 25 (1997): 293--304 [Extension of the Marton-Shields result to random fields in higher dimensions]
• Michael L. Stein, "When does the screening effect hold?", Annals of Statistics 39 (2011): 2795--2819
• Ne-Zheng Sun, "Structure reduction and robust experimental design for distributed parameter identification", Inverse Problems 21 (2005): 739--758
• Youngchul Sung, Lang Tong and H. Vincent Poor, "A Large Deviations Apoproach to Sensor Scheduling for Detection of Correlated Random Fields", cs.IT/0501056
• Lionel Truquet, "On a nonparametric resampling scheme for Markov random fields", Electronic Journal of Statistics 5 (2011): 1503--1536
• Nicolas Verzelen, "Adaptive estimation of stationary Gaussian fields", Annals of Statistics 38 (2010): 1363--1402
• Melanie W. Wall, "A close look at the spatial structure implied by the CAR and SAR models", Journal of Statistical Planning and Inference 121 (2004): 311-324
• Gerhard Winkler, Image Analysis, Random Fields, and Markov Chain Monte Carlo: A Mathematical Introduction
• Xiaoxi Zhang, Timothy D. Johnson, Roderick J. A. Little, Yue Cao, "Quantitative magnetic resonance image analysis via the EM algorithm with stochastic variation", Annals of Applied Statistics 2 (2008): 736--755, arxiv:0807.4672 [More of interest to me for the getting at uncertainty in estimation of hidden Markov random fields]