Log-Gaussian Cox process modeling of large spatial lightning data using spectral and Laplace approximations
暂无分享,去创建一个
David S. Matteson | Joseph Guinness | Megan L. Gelsinger | Maryclare Griffin | D. Matteson | J. Guinness | Maryclare Griffin
[1] David S. Henderson,et al. Evaluating Convective Initiation in High-Resolution Numerical Weather Prediction Models Using GOES-16 Infrared Brightness Temperatures , 2021, Monthly Weather Review.
[2] Jaewoo Park,et al. Reduced-dimensional Monte Carlo Maximum Likelihood for Latent Gaussian Random Field Models , 2019 .
[3] Matthias Katzfuss,et al. Vecchia-Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data , 2019, Comput. Stat. Data Anal..
[4] C. Kummerow,et al. A simplified method for the detection of convection using high-resolution imagery from GOES-16 , 2020, Atmospheric Measurement Techniques.
[5] M. Haran,et al. Fast expectation-maximization algorithms for spatial generalized linear mixed models , 2019, 1909.05440.
[6] Finn Lindgren,et al. inlabru: an R package for Bayesian spatial modelling from ecological survey data , 2019, Methods in Ecology and Evolution.
[7] Robert H. Holzworth,et al. Lightning: A New Essential Climate Variable , 2018, Eos.
[8] A. Blyth,et al. A projected decrease in lightning under climate change , 2018, Nature Climate Change.
[9] Murali Haran,et al. A Computationally Efficient Projection-Based Approach for Spatial Generalized Linear Mixed Models , 2016, Journal of Computational and Graphical Statistics.
[10] Spencer K. Clark,et al. Parameterization‐based uncertainty in future lightning flash density , 2017 .
[11] M. Fuentes,et al. Circulant Embedding of Approximate Covariances for Inference From Gaussian Data on Large Lattices , 2017 .
[12] Alan E. Gelfand,et al. Inference for log Gaussian Cox processes using an approximate marginal posterior , 2016, 1611.10359.
[13] Peter J. Diggle,et al. Bayesian Inference and Data Augmentation Schemes for Spatial, Spatiotemporal and Multivariate Log-Gaussian Cox Processes in R , 2015 .
[14] Peter J. Diggle,et al. lgcp: An R Package for Inference with Spatial and Spatio-Temporal Log-Gaussian Cox Processes , 2013 .
[15] Finn Lindgren,et al. Bayesian computing with INLA: New features , 2012, Comput. Stat. Data Anal..
[16] William J. Koshak,et al. The GOES-R GeoStationary Lightning Mapper (GLM) , 2012 .
[17] Haavard Rue,et al. A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA) , 2012, 1301.1817.
[18] Peter J. Diggle,et al. INLA or MCMC? A tutorial and comparative evaluation for spatial prediction in log-Gaussian Cox processes , 2012, 1202.1738.
[19] H. Rue,et al. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach , 2011 .
[20] H. Rue,et al. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations , 2009 .
[21] V. Kotroni,et al. Lightning occurrence in relation with elevation, terrain slope, and vegetation cover in the Mediterranean , 2008 .
[22] J. Beringer,et al. The Spatial and Temporal Distribution of Lightning Strikes and Their Relationship with Vegetation Type, Elevation, and Fire Scars in the Northern Territory , 2007 .
[23] Peter J. Diggle,et al. Point process methodology for on‐line spatio‐temporal disease surveillance , 2005 .
[24] J. Mecikalski,et al. Forecasting Convective Initiation by Monitoring the Evolution of Moving Cumulus in Daytime GOES Imagery , 2004 .
[25] P. Diggle,et al. Spatiotemporal prediction for log‐Gaussian Cox processes , 2001 .
[26] J. Møller,et al. Log Gaussian Cox Processes , 1998 .
[27] A. Wood,et al. Simulation of Stationary Gaussian Processes in [0, 1] d , 1994 .
[28] M. Hutchinson. A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines , 1989 .
[29] M. Hestenes,et al. Methods of conjugate gradients for solving linear systems , 1952 .