Computing the Jacobian in Gaussian Spatial Autoregressive Models: An Illustrated Comparison of Available Methods
暂无分享,去创建一个
[1] Stephan R. Sain,et al. spam: A Sparse Matrix R Package with Emphasis on MCMC Methods for Gaussian Markov Random Fields , 2010 .
[2] Ali Abul Gasim. First-order autoregressive models: a method for obtaining eigenvalues for weighting matrices , 1988 .
[3] Roger Bivand,et al. Exploring Spatial Data Analysis Techniques Using R: The Case of Observations with No Neighbors , 2004 .
[4] R. J. Martin. Approximations to the determinant term in gaussian maximum likelihood estimation of some spatial models , 1992 .
[5] K. Ord. Estimation Methods for Models of Spatial Interaction , 1975 .
[6] James P. LeSage,et al. Chebyshev approximation of log-determinants of spatial weight matrices , 2004, Comput. Stat. Data Anal..
[7] Timothy A. Davis,et al. Modifying a Sparse Cholesky Factorization , 1999, SIAM J. Matrix Anal. Appl..
[8] R. Pace,et al. Sparse spatial autoregressions , 1997 .
[9] Chao Yang,et al. ARPACK users' guide - solution of large-scale eigenvalue problems with implicitly restarted Arnoldi methods , 1998, Software, environments, tools.
[10] Luc Anselin,et al. An O(N) parallel method of computing the Log-Jacobian of the variable transformation for models with spatial interaction on a lattice , 2009, Comput. Stat. Data Anal..
[11] Daniel A. Griffith,et al. Faster maximum likelihood estimation of very large spatial autoregressive models: an extension of the Smirnov–Anselin result , 2004 .
[12] H. Rue. Fast sampling of Gaussian Markov random fields , 2000 .
[13] D. Griffith. Eigenfunction properties and approximations of selected incidence matrices employed in spatial analyses , 2000 .
[14] Noel Cressie,et al. One-step estimation of spatial dependence parameters: Properties and extensions of the APLE statistic , 2012, J. Multivar. Anal..
[15] Timothy A. Davis,et al. Algorithm 8 xx : a concise sparse Cholesky factorization package , 2004 .
[16] D. Griffith,et al. Trade-offs associated with normalizing constant computational simplifications for estimating spatial statistical models , 1995 .
[17] Brian D. Ripley,et al. Spatial Statistics: Ripley/Spatial Statistics , 2005 .
[18] L. Anselin,et al. Fast maximum likelihood estimation of very large spatial autoregression models: a characteristic polynomial approach , 2001 .
[19] Youngihn Kho,et al. GeoDa: An Introduction to Spatial Data Analysis , 2006 .
[20] Gottfried Tappeiner,et al. Performance contest between MLE and GMM for huge spatial autoregressive models , 2008 .
[21] Daniel A. Griffith,et al. A Variance-Stabilizing Coding Scheme for Spatial Link Matrices , 1999 .
[22] James P. LeSage,et al. A sampling approach to estimate the log determinant used in spatial likelihood problems , 2009, J. Geogr. Syst..
[23] Roger Bivand. Regression Modeling with Spatial Dependence: An Application of Some Class Selection and Estimation Methods , 2010 .
[24] Ronald P. Barry,et al. Quick Computation of Spatial Autoregressive Estimators , 2010 .
[25] R. Kelley Pace,et al. Fast spatial estimation , 1997 .
[26] Daniel A. Griffith,et al. Approximating the Inertia of the Adjacency Matrix of a Connected Planar Graph That Is the Dual of a Geographic Surface Partitioning , 2011 .
[27] Daniel A. Griffith,et al. Extreme eigenfunctions of adjacency matrices for planar graphs employed in spatial analyses , 2004 .
[28] Y. Zhang,et al. Approximate implementation of the logarithm of the matrix determinant in Gaussian process regression , 2007 .
[29] Barry W. Peyton,et al. Block Sparse Cholesky Algorithms on Advanced Uniprocessor Computers , 1991, SIAM J. Sci. Comput..
[30] H. Kelejian,et al. Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances , 2008, Journal of econometrics.
[31] Ronald P. Barry,et al. Monte Carlo estimates of the log determinant of large sparse matrices , 1999 .