Spatially varying coefficient modeling for large datasets: Eliminating N from spatial regressions
Tài liệu tham khảo
Anselin, 2010, Thirty years of spatial econometrics, Pap. Reg. Sci., 89, 3, 10.1111/j.1435-5957.2010.00279.x
Arbia, 2014, Pairwise likelihood inference for spatial regressions estimated on very large datasets, Spat. Stat., 7, 21, 10.1016/j.spasta.2013.10.001
Banerjee, 2008, Gaussian predictive process models for large spatial data sets, J. R. Stat. Soc. Ser. B, 70, 825, 10.1111/j.1467-9868.2008.00663.x
Bates, D.M., 2010. lme4: Mixed-effects modeling with R. http://lme4.r-forge.r-project.org/book.
Brunsdon, 1996, Geographically weighted regression: a method for exploring spatial nonstationarity, Geogr. Anal., 28, 281, 10.1111/j.1538-4632.1996.tb00936.x
Burden, 2015, The SAR model for very large datasets: a reduced rank approach, Econom., 3, 317
Cahill, 2007, Using geographically weighted regression to explore local crime patterns, Soc. Sci. Comput. Rev., 25, 174, 10.1177/0894439307298925
Cressie, 1993
Cressie, 2008, Fixed rank kriging for very large spatial data sets, J. R. Stat. Soc. Ser. B, 70, 209, 10.1111/j.1467-9868.2007.00633.x
Datta, 2016, Hierarchical nearest-neighbor Gaussian process models for large geostatistical datasets, J. Amer. Statist. Assoc., 111, 800, 10.1080/01621459.2015.1044091
Debarsy, 2018, Editorial for the special issue entitled: New advances in spatial econometrics: Interactions matter, Reg. Sci. Urban Econ., 10.1016/j.regsciurbeco.2018.02.004
Dong, 2018, Geographically weighted regression models for ordinal categorical response variables: An application to geo-referenced life satisfaction data, Comput. Environ. Urban Syst., 70, 35, 10.1016/j.compenvurbsys.2018.01.012
Dray, 2006, Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM), Ecol. Model., 196, 483, 10.1016/j.ecolmodel.2006.02.015
Drineas, 2005, On the Nyström method for approximating a gram matrix for improved kernel-based learning, J. Mach. Learn. Res., 6, 2153
Farber, 2007, A systematic investigation of cross-validation in GWR model estimation: empirical analysis and Monte Carlo simulations, J. Geogr. Sci., 9, 371
Finley, 2011, Comparing spatially-varying coefficients models for analysis of ecological data with non–stationary and anisotropic residual dependence, Methods Ecol. Evol., 2, 143, 10.1111/j.2041-210X.2010.00060.x
Finley, 2011, A hierarchical model for quantifying forest variables over large heterogeneous landscapes with uncertain forest areas, J. Amer. Statist. Assoc., 106, 31, 10.1198/jasa.2011.ap09653
Finley, 2009, Improving the performance of predictive process modeling for large datasets, Comput. Statist. Data Anal., 53, 2873, 10.1016/j.csda.2008.09.008
Fotheringham, 2002
Fotheringham, 2017, Multiscale geographically weighted regression (MGWR), Ann. Am. Assoc. Geogr., 107, 1247
Furrer, 2006, Covariance tapering for interpolation of large spatial datasets, J. Comput. Graph. Stat., 15, 502, 10.1198/106186006X132178
Gelfand, 2003, Spatial modeling with spatially varying coefficient processes, J. Amer. Statist. Assoc., 98, 378, 10.1198/016214503000170
Geniaux, 2018, A new method for dealing simultaneously with spatial autocorrelation and spatial heterogeneity in regression models, Reg. Sci. Urban Econ., 10.1016/j.regsciurbeco.2017.04.001
Goodchild, 2004, The validity and usefulness of laws in geographic information science and geography, Ann. Assoc. Am. Geogr., 94, 300, 10.1111/j.1467-8306.2004.09402008.x
Griffith, 2000, Eigenfunction properties and approximations of selected incidence matrices employed in spatial analyses, Linear Algebra Appl., 321, 95, 10.1016/S0024-3795(00)00031-8
Griffith, 2003
Griffith, 2004, Extreme eigenfunctions of adjacency matrices for planar graphs employed in spatial analyses, Linear Algebra Appl., 388, 201, 10.1016/S0024-3795(03)00368-9
Griffith, 2008, Spatial-filtering-based contributions to a critique of geographically weighted regression (GWR), Environ. Plann. A, 40, 2751, 10.1068/a38218
Griffith, 2015, Approximation of gaussian spatial autoregressive models for massive regular square tessellation data, Int. J. Geogr. Inf. Sci., 29, 2143, 10.1080/13658816.2015.1068318
Griffith, 2014, Spatial autocorrelation and spatial filtering, 1477
Harris, 2010, Robust geographically weighted regression: a technique for quantifying spatial relationships between freshwater acidification critical loads and catchment attributes, Ann. Assoc. Am. Geogr., 100, 286, 10.1080/00045600903550378
Harris, 2010, Grid - enabling geographically weighted regression: A case study of participation in higher education in England, Tran. GIS, 14, 43, 10.1111/j.1467-9671.2009.01181.x
Heaton, M.J., Datta, A., Finley, A., Furrer, R., Guhaniyogi, R., Gerber, F., Gramacy, R.B., Hammerling, D., Katzfuss, M., Lindgren, F., Nychka, D.W., Sun, F., Zammit-Mangion, A., 2017. A case study competition among methods for analyzing large spatial data. arxiv:1710.05013.
Helbich, 2016, Spatially varying coefficient models in real estate: Eigenvector spatial filtering and alternative approaches, Comput. Environ. Urban Syst., 57, 1, 10.1016/j.compenvurbsys.2015.12.002
Henderson, 1975, Best linear unbiased estimation and prediction under a selection model, Biometrics, 423, 10.2307/2529430
Jaya, 2016, Bayesian spatial modeling and mapping of dengue fever: A case study of dengue feverin the City of Bandung, Indonesia, Int. J. Appl. Math. Stat., 54, 94
Katzfuss, 2017, A multi-resolution approximation for massive spatial datasets, J. Am. Stat., 112, 201, 10.1080/01621459.2015.1123632
Kelejian, 1998, A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances, J. Real Estate Financ. Econ., 17, 99, 10.1023/A:1007707430416
LeSage, 2007, A matrix exponential spatial specification, J. Econom., 140, 190, 10.1016/j.jeconom.2006.09.007
LeSage, 2009
Li, 2015, Large-scale Nyström kernel matrix approximation using randomized SVD, IEEE Trans Neural Netw. Learn., 26, 152, 10.1109/TNNLS.2014.2359798
Li, 2018, Fast geographically weighted regression (FastGWR): a scalable algorithm to investigate spatial process heterogeneity in millions of observations, Int. J. Geogr. Inf. Sci., 32, 1, 10.1080/13658816.2018.1542697
Lindgren, 2015, Bayesian spatial modeling with R-INLA, J. Stat. Softw., 63, 1, 10.18637/jss.v063.i19
Lindgren, 2011, An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach, J. R. Stat. Soc. Ser. B, 73, 423, 10.1111/j.1467-9868.2011.00777.x
Liu, H., Ong, Y.S., Shen, X., Cai, J., 2018. When Gaussian process meets Big data: A review of scalable GPs. arXiv:1807.01065.
Lu, 2017, Geographically weighted regression with parameter-specific distance metrics, Int. J. Geogr. Inf. Sci., 31, 982, 10.1080/13658816.2016.1263731
Lu, 2014, Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data, Int. J. Geogr. Inform. Sci., 28, 660, 10.1080/13658816.2013.865739
Lu, 2014, The GWmodel R package: further topics for exploring spatial heterogeneity using geographically weighted models, Geo-spat. Inform. Sci., 17, 85, 10.1080/10095020.2014.917453
Lu, 2018, Improvements to the calibration of a geographically weighted regression with parameter-specific distance metrics and bandwidths, Comput. Environ. Urban Syst., 10.1016/j.compenvurbsys.2018.03.012
Mei, 2004, A note on the mixed geographically weighted regression model, J. Reg. Sci., 44, 143, 10.1111/j.1085-9489.2004.00331.x
Moran, 1950, Notes on continuous stochastic phenomena, Biometrika, 37, 17, 10.2307/2332142
Murakami, D., 2018. spmoran: An R package for Moran’s eigenvector-based spatial regression analysis. arxiv:1703.04467.
Murakami, 2015, Random effects specifications in eigenvector spatial filtering: a simulation study, J. Geogr. Sci., 17, 311
Murakami, 2018, Eigenvector spatial filtering for large data sets: fixed and random effects approaches, Geogr. Anal.
Murakami, 2019, The importance of scale in spatially varying coefficient modeling, Ann. Assoc. Am. Geogr., 109, 50
Murakami, 2017, A moran coefficient-based mixed effects approach to investigate spatially varying relationships, Spat. Stat., 19, 68, 10.1016/j.spasta.2016.12.001
Nakaya, 2005, Geographically weighted Poisson regression for disease associative mapping, Stat. Med., 24, 2695, 10.1002/sim.2129
Nakaya, 2009, Semiparametric geographically weighted generalized linear modelling in GWR 4.0
Nychka, 2015, A multiresolution gaussian process model for the analysis of large spatial datasets, J. Comput. Graph. Stat., 24, 579, 10.1080/10618600.2014.914946
Osei, 2017, Diarrhea morbidities in small areas: Accounting for non-stationarity in sociodemographic impacts using bayesian spatially varying coefficient modelling, Sci. Rep., 7, 9908, 10.1038/s41598-017-10017-6
Oshan, 2018, A comparison of spatially varying regression coefficient estimates using geographically weighted and spatial-filter-based techniques, Geogr. Anal., 50, 53, 10.1111/gean.12133
Paciorek, 2010, The importance of scale for spatial-confounding bias and precision of spatial regression estimators, Stat. Sci., 25, 107, 10.1214/10-STS326
Sang, 2012, A full scale approximation of covariance functions for large spatial data sets, J. R. Stat. Soc. Ser. B, 74, 111, 10.1111/j.1467-9868.2011.01007.x
Silvester, 2000, Determinants of block matrices, Math. Gazet, 84, 460, 10.2307/3620776
Smirnov, 2001, Fast maximum likelihood estimation of very large spatial autoregressive models: a characteristic polynomial approach, Comput. Statist. Data Anal., 35, 301, 10.1016/S0167-9473(00)00018-9
Stein, 2014, Limitations on low rank approximations for covariance matrices of spatial data, Spat. Stat., 8, 1, 10.1016/j.spasta.2013.06.003
Tiefelsdorf, 2007, Semiparametric filtering of spatial autocorrelation: the eigenvector approach, Environ. Plann. A, 39, 1193, 10.1068/a37378
Tobler, 1970, A computer movie simulating urban growth in the Detroit region, Econ. Geogr., 46, 234, 10.2307/143141
Tran, 2016, Large-scale geographically weighted regression on Spark, Proceedings of the 2016 International Conference on Knowledge and Systems Engineering (KSE), 127, 10.1109/KSE.2016.7758041
Umlauf, 2012, Structured additive regression models: An R interface to BayesX, J. Stat. Softw., 63, 1
Wheeler, 2007, An assessment of coefficient accuracy in linear regression models with spatially varying coefficients, J. Geogr. Sci., 9, 145
Wheeler, 2005, Multicollinearity and correlation among local regression coefficients in geographically weighted regression, J. Geogr. Sci., 7, 161
Wheeler, 2009, Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests, J. Geogr. Sci., 11, 1
Yang, 2014
Zhang, 2010, Clustered Nyström method for large scale manifold learning and dimension reduction, IEEE Trans. Neural Netw., 21, 1576, 10.1109/TNN.2010.2064786