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Estimation and Prediction using generalized Wendland Covariance Functions under fixed domain asymptotics

23 July 2016

Papers citing "Estimation and Prediction using generalized Wendland Covariance Functions under fixed domain asymptotics"

35 / 35 papers shown

Title
A class of modular and flexible covariate-based covariance functions for nonstationary spatial modeling Federico Blasi Reinhard Furrer 48 0 0 22 Oct 2024
Smoothness Estimation for Whittle-Matérn Processes on Closed Riemannian Manifolds Moritz Korte-Stapff Toni Karvonen Eric Moulines 52 0 0 31 Dec 2023
Transfer Learning for Bayesian Optimization on Heterogeneous Search Spaces Maria-Irina Nicolae Max Eisele Zehao Wang 65 9 0 28 Sep 2023
Comparing Scale Parameter Estimators for Gaussian Process Interpolation with the Brownian Motion Prior: Leave-One-Out Cross Validation and Maximum Likelihood Masha Naslidnyk Motonobu Kanagawa Toni Karvonen Maren Mahsereci GP 62 1 0 14 Jul 2023
Random Smoothing Regularization in Kernel Gradient Descent Learning Liang Ding Tianyang Hu Jiahan Jiang Donghao Li Wei Cao Yuan Yao 64 6 0 05 May 2023
HyperBO+: Pre-training a universal prior for Bayesian optimization with hierarchical Gaussian processes Z. Fan Xinran Han Zehao Wang 91 4 0 20 Dec 2022
Comparing two spatial variables with the probability of agreement Jonathan Acosta R. Vallejos A. Ellison Felipe Osorio Mário de Castro 28 1 0 15 Dec 2022
Radial Neighbors for Provably Accurate Scalable Approximations of Gaussian Processes Yicheng Zhu M. Peruzzi Cheng Li David B. Dunson 73 7 0 27 Nov 2022
Fixed-domain asymptotic properties of maximum composite likelihood estimators for max-stable Brown-Resnick random fields Nicolas Chenavier C. Robert 41 0 0 19 Sep 2022
Nonseparable Space-Time Stationary Covariance Functions on Networks cross Time Emilio Porcu P. White M. Genton 50 2 0 05 Aug 2022
Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posed Toni Karvonen Chris J. Oates GP 78 26 0 17 Mar 2022
Multivariate Gaussian Random Fields over Generalized Product Spaces involving the Hypertorus François Bachoc A. Peron Emilio Porcu 41 3 0 22 Feb 2022
Smooth Nested Simulation: Bridging Cubic and Square Root Convergence Rates in High Dimensions Wei Cao Yanyuan Wang Xiaowei Zhang 19 5 0 09 Jan 2022
Asymptotic analysis of ML-covariance parameter estimators based on covariance approximations Reinhard Furrer Michael Hediger 36 3 0 23 Dec 2021
A deep look into the Dagum family of isotropic covariance functions Tarik Faouzi Emilio Porcu I. Kondrashuk A. Malyarenko 16 7 0 28 Jun 2021
Measuring the robustness of Gaussian processes to kernel choice William T. Stephenson S. Ghosh Tin D. Nguyen Mikhail Yurochkin Sameer K. Deshpande Tamara Broderick GP 35 11 0 11 Jun 2021
Convergence of Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression Wei Cao Bing-Yi Jing 55 6 0 20 Apr 2021
Inference for Gaussian Processes with Matérn Covariogram on Compact Riemannian Manifolds Didong Li Wenpin Tang Sudipto Banerjee 87 14 0 08 Apr 2021
The Gauss Hypergeometric Covariance Kernel for Modeling Second-Order Stationary Random Fields in Euclidean Spaces: its Compact Support, Properties and Spectral Representation Xavier Emery Alfredo Alegría 53 13 0 23 Jan 2021
Flexible Validity Conditions for the Multivariate Matérn Covariance in any Spatial Dimension and for any Number of Components Xavier Emery Emilio Porcu P. White 35 2 0 11 Jan 2021
Asymptotic analysis of maximum likelihood estimation of covariance parameters for Gaussian processes: an introduction with proofs François Bachoc 54 13 0 15 Sep 2020
Unifying Compactly Supported and Matern Covariance Functions in Spatial Statistics M. Bevilacqua Christian Caamaño-Carrillo Emilio Porcu 54 30 0 06 Aug 2020
Asymptotically Equivalent Prediction in Multivariate Geostatistics François Bachoc Emilio Porcu M. Bevilacqua Reinhard Furrer Tarik Faouzi 27 7 0 29 Jul 2020
Necessary and sufficient conditions for asymptotically optimal linear prediction of random fields on compact metric spaces Kristin Kirchner David Bolin 20 10 0 18 May 2020
A multi-resolution approximation via linear projection for large spatial datasets Toshihiro Hirano 15 1 0 10 Apr 2020
On the Inference of Applying Gaussian Process Modeling to a Deterministic Function Wei Cao 88 19 0 04 Feb 2020
LASSO estimation for spherical autoregressive processes Alessia Caponera C. Durastanti Anna Vidotto 58 6 0 26 Nov 2019
Beyond Matérn: On A Class of Interpretable Confluent Hypergeometric Covariance Functions P. Ma A. Bhadra 57 11 0 14 Nov 2019
Kriging prediction with isotropic Matérn correlations: Robustness and experimental design Rui Tuo Wei Cao 107 37 0 13 Nov 2019
On identifiability and consistency of the nugget in Gaussian spatial process models Wenpin Tang Lu Zhang Sudipto Banerjee 83 40 0 15 Aug 2019
KTBoost: Combined Kernel and Tree Boosting Fabio Sigrist 85 27 0 11 Feb 2019
Non-Gaussian Geostatistical Modeling using (skew) t Processes M. Bevilacqua Christian Caamaño-Carrillo R. Arellano-Valle Víctor Morales-Oñate 146 20 0 15 Dec 2018
Composite likelihood estimation for a gaussian process under fixed domain asymptotics François Bachoc M. Bevilacqua D. Velandia 51 12 0 24 Jul 2018
Maximum likelihood estimation for Gaussian processes under inequality constraints François Bachoc A. Lagnoux A. F. López-Lopera 87 24 0 10 Apr 2018
Modeling Temporally Evolving and Spatially Globally Dependent Data Emilio Porcu Alfredo Alegría Reinhard Furrer 43 82 0 28 Jun 2017