Suchergebnisse - "Inference from spatial processes"

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

Autoren: Li, Jie Song, Yunquan Jian, Ling

Quelle: Statistics and Computing. 34

Schlagwörter: 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

Dateibeschreibung: application/xml

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

Autoren: Justin M. Leach Inmaculada Aban Nengjun Yi

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

Schlagwörter: 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

Dateibeschreibung: application/xml

Zugangs-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

Bayesian spatiotemporal modeling for inverse problems

Autoren: Shiwei Lan Shuyi Li Mirjeta Pasha

Quelle: Statistics and Computing. 33

Schlagwörter: chaotic dynamics, FOS: Computer and information sciences, Inference from spatial processes, uncertainty quantification, Bayesian inference, 01 natural sciences, trajectory fitting, Strange attractors, chaotic dynamics of systems with hyperbolic behavior, spatiotemporal inverse problems, Methodology (stat.ME), spatiotemporal Gaussian process, 0103 physical sciences, 0101 mathematics, Computational methods for problems pertaining to statistics, Statistics - Methodology

Dateibeschreibung: application/xml

Zugangs-URL: http://arxiv.org/abs/2204.10929

Spatial long memory

Autoren: Robinson, Peter

Quelle: Japanese Journal of Statistics and Data Science. 3:243-256

Schlagwörter: 2. Zero hunger, Time series, auto-correlation, regression, etc. in statistics (GARCH), Inference from spatial processes, spatial data, long memory, 0101 mathematics, 01 natural sciences, Directional data, spatial statistics

Dateibeschreibung: application/xml

Zugangs-URL: https://link.springer.com/content/pdf/10.1007/s42081-019-00061-z.pdf
http://eprints.lse.ac.uk/102182/
https://link.springer.com/content/pdf/10.1007/s42081-019-00061-z.pdf
https://link.springer.com/article/10.1007/s42081-019-00061-z
http://eprints.lse.ac.uk/102182/

A transition model for analyzing multivariate longitudinal data using Gaussian copula approach

Autoren: Mojtaba Ganjali Taban Baghfalaki

Quelle: AStA Advances in Statistical Analysis. 104:169-223

Schlagwörter: Inference from spatial processes, 05 social sciences, longitudinal study, transition model, copula function, 01 natural sciences, Applications of statistics to biology and medical sciences, meta analysis, 3. Good health, joint modeling, Time series, auto-correlation, regression, etc. in statistics (GARCH), ordinal regression, mixed data, 0502 economics and business, 0101 mathematics, Characterization and structure theory for multivariate probability distributions, copulas, Directional data, spatial statistics

Dateibeschreibung: application/xml

Zugangs-URL: https://ideas.repec.org/a/spr/alstar/v104y2020i2d10.1007_s10182-018-00346-w.html
https://link.springer.com/article/10.1007%2Fs10182-018-00346-w

Locally stationary spatio-temporal processes

Autoren: Yasumasa Matsuda Yoshihiro Yajima

Quelle: Japanese Journal of Statistics and Data Science. 1:41-57

Schlagwörter: spatially dependent spectral density function, Whittle likelihood estimation, Inference from spatial processes, seasonal AR model, compound Poisson, spatial nonstationarity, 01 natural sciences, locally stationary process, Time series, auto-correlation, regression, etc. in statistics (GARCH), CARMA kernel, 13. Climate action, Inference from stochastic processes and spectral analysis, 0101 mathematics, Applications of statistics to environmental and related topics

Dateibeschreibung: application/xml

Zugangs-URL: http://altmetrics.ceek.jp/article/hdl.handle.net/10097/00122062
https://link.springer.com/article/10.1007/s42081-018-0003-9/fulltext.html
https://link.springer.com/article/10.1007/s42081-018-0003-9

Robust Two-Stage Estimation in General Spatial Dynamic Panel Data Models: Robust two-stage estimation in general spatial dynamic panel data models

Autoren: Ding, Hao Jin, Baisuo Wu, Yuehua

Quelle: SSRN Electronic Journal.

Schlagwörter: model selection, Inference from spatial processes, consistency, M-estimation, asymptotic normality, outliers, Robustness and adaptive procedures (parametric inference), Asymptotic properties of parametric estimators, Applications of statistics to biology and medical sciences, meta analysis

Dateibeschreibung: application/xml

Zugangs-URL: https://zbmath.org/7903333
https://doi.org/10.1007/s11424-023-2172-2

A FREQUENCY DOMAIN APPROACH FOR THE ESTIMATION OF PARAMETERS OF SPATIO‐TEMPORAL STATIONARY RANDOM PROCESSES: A frequency domain approach for the estimation of parameters of spatio-temporal stationary random processes

Autoren: Georgi N. Boshnakov Tata Subba Rao Sourav Das

Quelle: Subba Rao, T, Das, S & Boshnakov, G N 2014, 'A Frequency domain approach for the estimation of parameters of spatio-temporal stationary random processes', Journal of Time Series Analysis, vol. 35, no. 4, pp. 357-377. https://doi.org/10.1111/jtsa.12069

Schlagwörter: Time series, auto-correlation, regression, etc. in statistics (GARCH), Inference from spatial processes, discrete Fourier transform, 4. Education, composite likelihood, frequency variogram, 0101 mathematics, Non-Markovian processes: estimation, Applications of statistics to environmental and related topics, 16. Peace & justice, 01 natural sciences

Dateibeschreibung: application/xml; application/pdf

Zugangs-URL: http://eprints.maths.manchester.ac.uk/1879/1/RaoDasBoshnakov_Whittle_Likelihood.pdf
https://research.manchester.ac.uk/en/publications/bfac3d54-304c-47c8-ba52-49ad5cae6d42
https://doi.org/10.1111/jtsa.12069
https://www.escholar.manchester.ac.uk/uk-ac-man-scw:255728
http://eprints.ma.man.ac.uk/1879/
https://core.ac.uk/display/74167200
https://ideas.repec.org/a/bla/jtsera/v35y2014i4p357-377.html
https://maths.manchester.ac.uk/goran/research-reports/psrr07-2012.pdf
https://econpapers.repec.org/RePEc:bla:jtsera:v:35:y:2014:i:4:p:357-377

Bayesian Spatial Transformation Models with Applications in Neuroimaging Data: Bayesian spatial transformation models with applications in neuroimaging data

Autoren: Joseph G. Ibrahim Hongtu Zhu Michelle F. Miranda

Quelle: Biometrics. 69:1074-1083

Schlagwörter: FOS: Computer and information sciences, Inference from spatial processes, MCMC, Bayesian inference, Bayesian analysis, Neuroimaging, Statistics - Applications, 01 natural sciences, Gaussian Markov random fields, Applications of statistics to biology and medical sciences, meta analysis, Pattern Recognition, Automated, Box-Cox transformation, Spatio-Temporal Analysis, big data, Image Interpretation, Computer-Assisted, Humans, Computer Simulation, Applications (stat.AP), 0101 mathematics, Child, Models, Statistical, Brain, Random fields, image analysis, Bayes Theorem, Attention Deficit Disorder with Hyperactivity, Numerical analysis or methods applied to Markov chains, Nerve Net

Dateibeschreibung: application/xml

Zugangs-URL: https://europepmc.org/articles/pmc3864982?pdf=render
https://pubmed.ncbi.nlm.nih.gov/24128143
http://arxiv.org/abs/1607.07664
https://zbmath.org/6281637
https://doi.org/10.1111/biom.12085
https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.12085
https://mdanderson.elsevierpure.com/en/publications/bayesian-spatial-transformation-models-with-applications-in-neuro
https://econpapers.repec.org/article/blabiomet/v_3a69_3ay_3a2013_3ai_3a4_3ap_3a1074-1083.htm
https://ideas.repec.org/a/bla/biomet/v69y2013i4p1074-1083.html
https://mdanderson.influuent.utsystem.edu/en/publications/bayesian-spatial-transformation-models-with-applications-in-neuro
http://europepmc.org/articles/PMC3864982
https://arxiv.org/pdf/1607.07664
https://ui.adsabs.harvard.edu/abs/2016arXiv160707664M/abstract
https://arxiv.org/abs/1607.07664

Elephant‐seal movements: Modelling migration: Elephant-seal movements: Modelling migration

Autoren: Brent S. Stewart David R. Brillinger

Quelle: Selected Works of David Brillinger ISBN: 9781461413431

Schlagwörter: animal migration, 0106 biological sciences, Inference from spatial processes, drift, Markov processes: estimation, hidden Markov models, likelihood, stochastic differential equation, 16. Peace & justice, 01 natural sciences, Applications of statistics to biology and medical sciences, meta analysis, 13. Climate action, 14. Life underwater, 0101 mathematics, diffusion process, great-circle route, Applications of Brownian motions and diffusion theory (population genetics, absorption problems, etc.), spherical motion

Dateibeschreibung: application/xml

Zugangs-URL: https://zbmath.org/1229572
https://doi.org/10.2307/3315767
https://link.springer.com/chapter/10.1007/978-1-4614-1344-8_23
https://www.jstor.org/stable/3315767
https://onlinelibrary.wiley.com/doi/abs/10.2307/3315767
https://core.ac.uk/display/24493154
https://rd.springer.com/chapter/10.1007/978-1-4614-1344-8_23

Spatial–temporal association between fine particulate matter and daily mortality: Spatial-temporal association between fine particulate matter and daily mortality

Autoren: Brian J. Reich Montserrat Fuentes Jungsoon Choi

Quelle: Computational Statistics & Data Analysis. 53:2989-3000

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine, Inference from spatial processes, 13. Climate action, Bayesian inference, 11. Sustainability, 0101 mathematics, Computational methods for problems pertaining to statistics, 01 natural sciences, Applications of statistics to biology and medical sciences, meta analysis, 3. Good health

Dateibeschreibung: application/xml

Zugangs-URL: https://europepmc.org/articles/pmc2685284?pdf=render
https://pubmed.ncbi.nlm.nih.gov/19652691
https://zbmath.org/5689063
https://doi.org/10.1016/j.csda.2008.05.018

Bayesian identification, selection and estimation of semiparametric functions in high-dimensional additive models

Autoren: PANAGIOTELIS, A. SMITH, M.

Quelle: Journal of Econometrics. 143:291-316

Schlagwörter: Bayesian variable selection, intrinsic Gaussian Markov random fields, Inference from spatial processes, P-splines, Bayesian inference, 05 social sciences, 01 natural sciences, regularization, Density estimation, Bayesian semiparametric regression, seemingly unrelated regression, intra-day electricity load modelling, 0502 economics and business, Numerical analysis or methods applied to Markov chains, Econometrics, Nonparametric regression and quantile regression, 0101 mathematics, Applications of statistics to economics

Dateibeschreibung: application/xml

Zugangs-URL: https://zbmath.org/6589675
https://doi.org/10.1016/j.jeconom.2007.10.003
https://minerva-access.unimelb.edu.au/handle/11343/29490?show=full
https://www.sciencedirect.com/science/article/pii/S030440760700214X
https://findanexpert.unimelb.edu.au/scholarlywork/305882-bayesian-identification--selection-and-estimation-of-semiparametric-functions-in-high-dimensional-additive-models
https://EconPapers.repec.org/RePEc:eee:econom:v:143:y:2008:i:2:p:291-316
https://ideas.repec.org/a/eee/econom/v143y2008i2p291-316.html
https://research.monash.edu/en/publications/bayesian-identification-selection-and-estimation-of-semiparametri

Multi-parameter auto-models with applications to cooperative systems

Autoren: Hardouin, Cécile Yao, Jian-Feng Statistique Appliquée et MOdélisation Stochastique (SAMOS) et al.

Weitere Verfasser: Hardouin, Cécile Yao, Jian-Feng Statistique Appliquée et MOdélisation Stochastique (SAMOS) et al.

Quelle: Comptes Rendus. Mathématique. 345:349-352

Schlagwörter: Inference from spatial processes, [STAT.TH] Statistics [stat]/Statistics Theory [stat.TH], 05 social sciences, Random fields, image analysis, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], Automodels, 01 natural sciences, spatial cooperation, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], exponential family, 0502 economics and business, 0101 mathematics, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]

Dateibeschreibung: application/xml; application/pdf

Zugangs-URL: https://hal.archives-ouvertes.fr/hal-00177184/file/Hardouin.pdf
https://zbmath.org/5198206
https://doi.org/10.1016/j.crma.2007.07.019
https://hal.archives-ouvertes.fr/hal-00177184/document
https://hal.archives-ouvertes.fr/hal-00177184
https://www.sciencedirect.com/science/article/pii/S1631073X07003275
http://hub.hku.hk/handle/10722/132613
https://hal.science/hal-00177184v1
https://hal.science/hal-00177184v1/document
https://doi.org/10.1016/j.crma.2007.07.019
http://hdl.handle.net/10722/132613

A Mixed Mover–Stayer Model for Spatiotemporal Two‐State Processes: A mixed mover-stayer model for spatiotemporal two-state processes

Autoren: Farouk S. Nathoo Charmaine B. Dean

Quelle: Biometrics. 63:881-891

Schlagwörter: Models, Statistical, Inference from spatial processes, Monte Carlo EM algorithm, mixed Markov model, spatiotemporal binary data, Bayesian inference, Communicable Diseases, Models, Biological, Risk Assessment, 01 natural sciences, Applications of statistics to biology and medical sciences, meta analysis, intrinsic autoregressive model, Time series, auto-correlation, regression, etc. in statistics (GARCH), Recurrence, Risk Factors, Data Interpretation, Statistical, Chronic Disease, Humans, Numerical analysis or methods applied to Markov chains, Computer Simulation, 0101 mathematics, Monte Carlo Method, Algorithms

Dateibeschreibung: application/xml

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/17825018
https://zbmath.org/5225395
https://doi.org/10.1111/j.1541-0420.2007.00752.x
https://www.jstor.org/stable/4541421
https://www.ncbi.nlm.nih.gov/pubmed/17825018
http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2007.00752.x/abstract
https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1541-0420.2007.00752.x
https://econpapers.repec.org/RePEc:bla:biomet:v:63:y:2007:i:3:p:881-891
https://ideas.repec.org/a/bla/biomet/v63y2007i3p881-891.html

Statistical inference for spatial statistics defined in the Fourier domain

Autoren: Subba Rao, Suhasini

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

Schlagwörter: 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

Dateibeschreibung: application/xml; application/pdf

Zugangs-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

Testing for separability of spatial–temporal covariance functions: Testing for separability of spatial\,-\,temporal covariance functions

Autoren: Montserrat Fuentes

Quelle: Journal of Statistical Planning and Inference. 136:447-466

Schlagwörter: Non-Markovian processes: hypothesis testing, Inference from spatial processes, Covariance, 05 social sciences, Analysis of variance and covariance (ANOVA), 01 natural sciences, Spectral density, 0502 economics and business, Fourier transform, Inference from stochastic processes and spectral analysis, 0101 mathematics, Applications of statistics to environmental and related topics, Periodogram, Weakly stationarity

Dateibeschreibung: application/xml

Zugangs-URL: https://repository.lib.ncsu.edu/bitstream/1840.4/206/1/tests.pdf
https://repository.lib.ncsu.edu/bitstream/1840.4/206/1/tests.pdf
https://www.sciencedirect.com/science/article/pii/S0378375804003210
https://repository.lib.ncsu.edu/handle/1840.4/206

Maximum likelihood for the fully observed contact process

Autoren: Willem R. van Zwet Marta Fiocco

Quelle: Journal of Computational and Applied Mathematics. 186:117-129

Schlagwörter: Inference from spatial processes, contact process, supercritical contact process, Applied Mathematics, Supercritical contact process, Interacting random processes, statistical mechanics type models, percolation theory, counting process, Non-Markovian processes: estimation, Counting process, 01 natural sciences, Contact process, Computational Mathematics, conditional asymptotics, maximum likelihood, 0101 mathematics, Maximum likelihood

Dateibeschreibung: application/xml

Zugangs-URL: https://zbmath.org/2221512
https://doi.org/10.1016/j.cam.2005.01.037
https://www.sciencedirect.com/science/article/pii/S0377042705001925
https://core.ac.uk/display/82104858
https://www.sciencedirect.com/science/article/abs/pii/S0377042705001925
https://ui.adsabs.harvard.edu/abs/2006JCoAM.186..117F/abstract

Outliers detection in multivariate spatial linear models

Autoren: Ana F. Militino M. B. Palacios María Dolores Ugarte

Quelle: Journal of Statistical Planning and Inference. 136:125-146

Schlagwörter: Inference from spatial processes, 0502 economics and business, 05 social sciences, Monte Carlo methods, Geostatistics, Forward search, Universal cokriging, 0101 mathematics, Robustness, Influence measures, 01 natural sciences, Directional data, spatial statistics

Dateibeschreibung: application/xml

Zugangs-URL: https://zbmath.org/2245257
https://doi.org/10.1016/j.jspi.2004.06.033
https://www.sciencedirect.com/science/article/pii/S0378375804002988

Central limit theorems for smoothed extreme value estimates of Poisson point processes boundaries: Central limit theorems for smoothed extreme value estimates of Poisson point processes bound\-aries

Autoren: Girard, Stéphane Menneteau, Ludovic Girard, Stephane et al.

Weitere Verfasser: Girard, Stéphane Menneteau, Ludovic Girard, Stephane et al.

Quelle: Journal of Statistical Planning and Inference. 135:433-460

Schlagwörter: 0209 industrial biotechnology, [STAT.ME] Statistics [stat]/Methodology [stat.ME], Inference from spatial processes, functional estimate, Mathematics - Statistics Theory, Poisson process, Statistics Theory (math.ST), 02 engineering and technology, 01 natural sciences, Extreme value theory, extremal stochastic processes, boundary estimation, Asymptotic properties of nonparametric inference, FOS: Mathematics, 0101 mathematics, Nonparametric estimation, [STAT.ME]Statistics [stat]/Methodology [stat.ME]

Dateibeschreibung: application/xml; application/pdf

Zugangs-URL: http://arxiv.org/pdf/1103.5884
http://arxiv.org/abs/1103.5884
https://inria.hal.science/hal-00383141v2/document
https://inria.hal.science/hal-00383141v2
https://doi.org/10.1016/j.jspi.2004.04.020
https://hal.archives-ouvertes.fr/hal-00383141
https://hal.inria.fr/hal-00958662/document
https://hal.inria.fr/hal-00958662
https://www.sciencedirect.com/science/article/pii/S0378375804002290

Simulated annealing and object point processes: Tools for analysis of spatial patterns

Autoren: Stoica, R.S. Gregori, P. Mateu, J. et al.

Weitere Verfasser: Stoica, R.S. Gregori, P. Mateu, J. et al.

Quelle: Stochastic Processes and their Applications. 115:1860-1882

Schlagwörter: Statistics and Probability, Inference from spatial processes, Cooling schedules, MODELE BISOUS, Three-dimensional spatial patterns, Stochastic optimization, Bisous model, [MATH] Mathematics [math], [INFO] Computer Science [cs], Object point process, 01 natural sciences, Simulated annealing, cooling schedule, Modelling and Simulation, Computational methods in Markov chains, 0103 physical sciences, [INFO]Computer Science [cs], [MATH]Mathematics [math], marked point process, 0101 mathematics, Markov chain Monte Carlo simulation, Marked point process, object point process, Applied Mathematics, Random fields, image analysis, METHODE MONTE CARLO, Numerical analysis or methods applied to Markov chains, Point processes (e.g., Poisson, Cox, Hawkes processes), simulated annealing

Dateibeschreibung: application/xml

Zugangs-URL: https://zbmath.org/2229463
https://doi.org/10.1016/j.spa.2005.06.007
https://ideas.repec.org/a/eee/spapps/v115y2005i11p1860-1882.html
https://www.sciencedirect.com/science/article/pii/S0304414905000839
https://econpapers.repec.org/RePEc:eee:spapps:v:115:y:2005:i:11:p:1860-1882
https://core.ac.uk/display/82228946
https://hal.inrae.fr/hal-02680509v1