Search Results - Inference from spatial processes

Likelihood and Non‐parametric Bayesian MCMC Inference for Spatial Point Processes Based on Perfect Simulation and Path Sampling: Likelihood and non-parametric Bayesian MCMC inference for spatial point processes based on perfect simulation and path sampling

Authors: Berthelsen, Kasper Klitgaard Møller, Jesper

Source: Berthelsen, K K & Møller, J 2003, 'Likelihood and non-parametric Bayesian MCMC inference for spatial point processes based on perfect simulation and path sampling', Scandinavian Journal of Statistics, vol. 30, no. 3, pp. 549-564.

Subject Terms: Inference from spatial processes, Strauss process, Nonparametric statistical resampling methods, Numerical analysis or methods applied to Markov chains, maximum likelihood estimation, 0101 mathematics, Non-Markovian processes: estimation, 01 natural sciences, dominated coupling from the past

File Description: application/xml

Access URL: https://zbmath.org/2059392
https://doi.org/10.1111/1467-9469.00348
https://vbn.aau.dk/da/publications/399d5030-0022-11da-b4d5-000ea68e967b
https://onlinelibrary.wiley.com/doi/10.1111/1467-9469.00348
https://ideas.repec.org/a/bla/scjsta/v30y2003i3p549-564.html
https://vbn.aau.dk/da/publications/likelihood-and-non-parametric-bayesian-mcmc-inference-for-spatial
http://onlinelibrary.wiley.com/doi/10.1111/1467-9469.00348/abstract
https://core.ac.uk/display/24591222
https://econpapers.repec.org/RePEc:bla:scjsta:v:30:y:2003:i:3:p:549-564

Likelihood Inference for Gibbs Processes in the Analysis of Spatial Point Patterns*: Likelihood inference for Gibbs processes in the analysis of spatial point patterns

Authors: Jorge Mateu Francisco Montes

Source: International Statistical Review. 69:81-104

Subject Terms: Inference from spatial processes, Point estimation, 0101 mathematics, 01 natural sciences

File Description: application/xml

Access URL: https://zbmath.org/2124677
https://doi.org/10.1111/j.1751-5823.2001.tb00481.x
https://ideas.repec.org/a/bla/istatr/v69y2001i1p81-104.html
https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1751-5823.2001.tb00481.x

Inference for Spatial Processes Using Subsampling: a Simulation Study

Authors: Kaiser, M Hsu, Nanjung Cressie, Noel A et al.

Source: Environmetrics. 8:485-502

Subject Terms: spatial, inference, 13. Climate action, 4. Education, processes, Physical Sciences and Mathematics, subsampling, study, 15. Life on land, simulation

Access URL: https://ro.uow.edu.au/infopapers/2385/

Statistical inference for spatial and spatio-temporal processes

Authors: Dimitriou-Fakalou, Chrysoula

Subject Terms: 519.5

Access URL: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437230

Deep neural networks for variable selection of higher-order nonparametric spatial autoregressive model

Authors: Li, Jie Song, Yunquan Jian, Ling

Source: Statistics and Computing. 34

Subject Terms: Ridge regression, shrinkage estimators (Lasso), Time series, auto-correlation, regression, etc. in statistics (GARCH), higher-order nonparametric spatial autoregressive model, Inference from spatial processes, deep neural networks, nonparametric endogenous effect, Nonparametric regression and quantile regression, Computational methods for problems pertaining to statistics, Artificial neural networks and deep learning, variable selection

File Description: application/xml

Inference from spatial processes

Authors: Gotway, Carol Noel A. C. Cressie Statistics

Contributors: Gotway, Carol Noel A. C. Cressie Statistics

Subject Terms: Statistics and Probability, Statistics, 15. Life on land

File Description: application/pdf

Access URL: https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=10040&context=rtd
https://lib.dr.iastate.edu/rtd/9041/

Incorporating spatial structure into inclusion probabilities for Bayesian variable selection in generalized linear models with the spike-and-slab elastic net

Authors: Justin M. Leach Inmaculada Aban Nengjun Yi

Source: Journal of Statistical Planning and Inference. 217:141-152

Subject Terms: Bayesian variable selection, Generalized linear models (logistic models), Ridge regression, shrinkage estimators (Lasso), Inference from spatial processes, Bayesian inference, generalized linear models, Alzheimer's disease, elastic net, 01 natural sciences, Applications of statistics to biology and medical sciences, meta analysis, spike-and-slab, 03 medical and health sciences, 0302 clinical medicine, 0101 mathematics, penalized likelihood

File Description: application/xml

Access URL: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
https://pubmed.ncbi.nlm.nih.gov/36911105
https://zbmath.org/7444359
https://doi.org/10.1016/j.jspi.2021.07.010
https://www.sciencedirect.com/science/article/pii/S037837582100077X

Data integration for inference about spatial processes: A model-based approach to test and account for data inconsistency.

Authors: Simone Tenan Paolo Pedrini Natalia Bragalanti et al.

Source: PLoS ONE, Vol 12, Iss 10, p e0185588 (2017)

Subject Terms: Medicine, Science

File Description: electronic resource

Relation: https://doaj.org/toc/1932-6203

Access URL: https://doaj.org/article/2e463d4e49c54b56a2d715945f48788d

Sur l'inférence statistique pour des processus spatiaux et spatio-temporels extrêmes

On statistical inference for spatial and spatio-temporal extreme processes

Authors: Abu-Awwad, Abdul-Fattah

Thesis Advisors: Lyon, Maume-Deschamps, Véronique, Ribereau, Pierre

Subject Terms: Dépendance/Indépendance asymptotique, Vraisemblance composite, Événement extrême, Fλ-madogramme, Processus max-stable, Processus max-mélange, Précipitations, Estimation semi-paramétrique, Asymptotic dependence/independence, Composite likelihood, Extreme event, Fλ- madogram, Max-stable process, Max-mixture process, Rainfall data, Semi-parametric estimation, 510

Availability: http://www.theses.fr/2019LYSE1079/document

SpaCcLink: exploring downstream signaling regulations with graph attention network for systematic inference of spatial cell–cell communication

Authors: Jingtao Liu Litian Ma Fen Ju et al.

Source: BMC Biology, Vol 23, Iss 1, Pp 1-15 (2025)

Subject Terms: Spatial transcriptome, Cell–cell communication, Downstream pathways, Communication patterns, Biology (General), QH301-705.5

File Description: electronic resource

Relation: https://doaj.org/toc/1741-7007

Access URL: https://doaj.org/article/6578dacf4f8f4bc6bf8543b97bc909ca

Bayesian Inference for Non-Stationary Spatial Covariance Structure via Spatial Deformations: Bayesian inference for non-stationary spatial covariance structure via spatial deformations

Authors: Anthony O'Hagan Alexandra M. Schmidt

Source: Journal of the Royal Statistical Society Series B: Statistical Methodology. 65:743-758

Subject Terms: augmented covariance matrix, Inference from spatial processes, spatial deformation, 13. Climate action, Bayesian inference, Numerical analysis or methods applied to Markov chains, 0101 mathematics, Applications of statistics to environmental and related topics, Gaussian process, anisotropic, 01 natural sciences, environmental monitoring

File Description: application/xml

Access URL: https://onlinelibrary.wiley.com/doi/abs/10.1111/1467-9868.00413
http://onlinelibrary.wiley.com/doi/10.1111/1467-9868.00413/abstract?globalMessage=0
https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/1467-9868.00413
http://onlinelibrary.wiley.com/doi/10.1111/1467-9868.00413/abstract
https://www.jstor.org/stable/3647549
https://EconPapers.repec.org/RePEc:bla:jorssb:v:65:y:2003:i:3:p:743-758

Spatial Causality: A Systematic Review on Spatial Causal Inference.

Authors: Akbari, Kamal Winter, Stephan Tomko, Martin

Source: Geographical Analysis; Jan2023, Vol. 55 Issue 1, p56-89, 34p

Subject Terms: CAUSAL inference, MEDIATION (Statistics)

Inference for spatial processes using imperfect data from measurements and numerical simulations

Authors: Youngman, BD Stephenson, DB

Subject Terms: Methodology (stat.ME), FOS: Computer and information sciences, 0101 mathematics, 01 natural sciences, Statistics - Methodology, 0105 earth and related environmental sciences

Access URL: http://arxiv.org/abs/1609.07714

JSS Journal of Statistical Software January 2013, Volume 52, Issue 4. http://www.jstatsoft.org/ lgcp : Inference with Spatial and Spatio-Temporal Log-Gaussian Cox Processes in R

Authors: Benjamin M. Taylor Tilman M. Davies Barry S. Rowlingson et al.

Source: Journal of Statistical Software, Vol 52, Iss 4 (2013)

Subject Terms: Cox process, spatio-temporal point process, Statistics, HA1-4737

File Description: electronic resource

Relation: http://www.jstatsoft.org/v52/i04/paper; https://doaj.org/toc/1548-7660

Access URL: https://doaj.org/article/bf6762deb0ec4f3f9df70c178f7c775b

Adjusted Euler--MacLaurin Predictor for Integrating Smooth Spatial Processes: Adjusted Euler-Maclaurin predictor for integrating smooth spatial processes

Authors: R. Drouilhet$^dag$ K. Benhenni

Source: Theory of Probability & Its Applications. 48:506-520

Subject Terms: Inference from spatial processes, spatial stationary process, Euler-Maclaurin formula, predictor, Prediction theory (aspects of stochastic processes), 0101 mathematics, 01 natural sciences, Inference from stochastic processes and prediction

File Description: application/xml

Access URL: https://epubs.siam.org/doi/abs/10.1137/S0040585X97980609
https://dialnet.unirioja.es/servlet/articulo?codigo=1015253

Diagnostics for Pairwise Extremal Dependence in Spatial Processes: Diagnostics for pairwise extremal dependence in spatial processes

Authors: Ancona-Navarrete, Miguel A. Tawn, Jonathan A.

Source: Extremes. 5:271-285

Subject Terms: pairwise extremal dependence, Inference from spatial processes, Statistics of extreme values, tail inference, extremal dependence, asymptotic independence, 0101 mathematics, Applications of statistics to environmental and related topics, 01 natural sciences, spatial processes, coefficient of tail dependence, 0105 earth and related environmental sciences

File Description: application/xml

Access URL: https://zbmath.org/2059070
https://doi.org/10.1023/a:1024029128431
https://eprints.lancs.ac.uk/id/eprint/9677/
https://link.springer.com/article/10.1023%2FA%3A1024029128431

A Spatial Causal Inference Analysis Framework

Authors: Akbari, Kamal

Index Terms: Spatial Analysis, Spatial Causal Inference, Geographic Information Science, Spatial Information, Spatial Data, Spatial Statistics, Spatial causal Inference Framework, Spatial Data Science, Geospatial Data Science, PhD thesis

URL: http://hdl.handle.net/11343/340437

Perfect simulation and inference for point processes given noisy observations

Authors: Elke Thönnes Jens Lund

Source: Computational Statistics. 19:317-336

Subject Terms: Other computational problems in probability, spatial birth-and-death process, Inference from spatial processes, noisy point process, 0202 electrical engineering, electronic engineering, information engineering, Point processes (e.g., Poisson, Cox, Hawkes processes), 02 engineering and technology, marked point process, 0101 mathematics, 01 natural sciences, dominated coupling from the past

File Description: application/xml

Access URL: https://dblp.uni-trier.de/db/journals/cstat/cstat19.html#LundT04
https://link.springer.com/article/10.1007%2FBF02892063

A Non‐Gaussian Spatial Process Model for Opacity of Flocculated Paper: A non-Gaussian spatial process model for opacity of flocculated paper

Authors: Brown, Patrick E. Diggle, Peter J. Henderson, Robin

Source: Scandinavian Journal of Statistics. 30:355-368

Subject Terms: 2. Zero hunger, Inference from spatial processes, 4. Education, Applications of statistics in engineering and industry, control charts, Random fields, image analysis, 15. Life on land, 01 natural sciences, spectral analysis, Matérn functions, 12. Responsible consumption, 13. Climate action, paper formation, random field, 11. Sustainability, Inference from stochastic processes and spectral analysis, 0101 mathematics, spatial stochastic model, Poisson cluster process

File Description: application/xml

Access URL: https://zbmath.org/2118811
https://doi.org/10.1111/1467-9469.00335
https://dialnet.unirioja.es/servlet/articulo?codigo=2843094
https://jhu.pure.elsevier.com/en/publications/a-non-gaussian-spatial-process-model-for-opacity-of-flocculated-p-4
https://eprints.lancs.ac.uk/id/eprint/9684/
https://EconPapers.repec.org/RePEc:bla:scjsta:v:30:y:2003:i:2:p:355-368
https://onlinelibrary.wiley.com/doi/abs/10.1111/1467-9469.00335
https://ideas.repec.org/a/bla/scjsta/v30y2003i2p355-368.html

Statistical inference for spatial statistics defined in the Fourier domain

Authors: Subba Rao, Suhasini

Source: Ann. Statist. 46, no. 2 (2018), 469-499

Subject Terms: Inference from spatial processes, Asymptotic distribution theory in statistics, Mathematics - Statistics Theory, Statistics Theory (math.ST), spectral density function, 01 natural sciences, quadratic forms, irregular spaced locations, Time series, auto-correlation, regression, etc. in statistics (GARCH), 62M15, FOS: Mathematics, spatial spectral density function, random fields, stationary spatial process, stationary spatial random fields, Inference from stochastic processes and spectral analysis, 62M30, increasing domain asymptotics, 0101 mathematics, fixed domain asymptotics, Fixed and increasing domain asymptotics

File Description: application/xml; application/pdf

Access URL: https://projecteuclid.org/journals/annals-of-statistics/volume-46/issue-2/Statistical-inference-for-spatial-statistics-defined-in-the-Fourier-domain/10.1214/17-AOS1556.pdf
http://arxiv.org/abs/1405.5240
https://ui.adsabs.harvard.edu/abs/2014arXiv1405.5240S/abstract
https://projecteuclid.org/journals/annals-of-statistics/volume-46/issue-2/Statistical-inference-for-spatial-statistics-defined-in-the-Fourier-domain/10.1214/17-AOS1556.full
https://projecteuclid.org/download/pdfview_1/euclid.aos/1522742426
https://projecteuclid.org/euclid.aos/1522742426