Pages that link to "Item:Q3631452"
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The following pages link to Fixed Rank Kriging for Very Large Spatial Data Sets (Q3631452):
Displaying 50 items.
- An area-specific stick breaking process for spatial data (Q2633423) (← links)
- Properties and comparison of some kriging sub-model aggregation methods (Q2676512) (← links)
- Bayesian Nonparametric Models (Q2800187) (← links)
- Interpolation of spatial data -- a stochastic or a deterministic problem? (Q2870835) (← links)
- Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations (with discussion) (Q2920273) (← links)
- A pseudo-penalized quasi-likelihood approach to the spatial misalignment problem with non-normal data (Q2927618) (← links)
- A Class of Convolution-Based Models for Spatio-Temporal Processes with Non-Separable Covariance Structure (Q3103140) (← links)
- Isotropic covariance matrix polynomials on spheres (Q3185984) (← links)
- Modeling Bronchiolitis Incidence Proportions in the Presence of Spatio-Temporal Uncertainty (Q3304834) (← links)
- Spherical Data Handling and Analysis with R package rcosmo (Q3305508) (← links)
- Iteratively reweighted least squares with random effects for maximum likelihood in generalized linear mixed effects models (Q3390328) (← links)
- Hierarchical Low Rank Approximation of Likelihoods for Large Spatial Datasets (Q3391136) (← links)
- A Computationally Efficient Projection-Based Approach for Spatial Generalized Linear Mixed Models (Q3391151) (← links)
- Dynamically Updated Spatially Varying Parameterizations of Hierarchical Bayesian Models for Spatial Data (Q3391187) (← links)
- Fast Nonseparable Gaussian Stochastic Process With Application to Methylation Level Interpolation (Q3391424) (← links)
- Gaussian Predictive Process Models for Large Spatial Data Sets (Q3631475) (← links)
- Hierarchical Spatial Modeling of Additive and Dominance Genetic Variance for Large Spatial Trial Datasets (Q3636988) (← links)
- Uncertainty Quantification Using the Nearest Neighbor Gaussian Process (Q4556968) (← links)
- Fast Spatial Gaussian Process Maximum Likelihood Estimation via Skeletonization Factorizations (Q4601605) (← links)
- Gaussian processes for computer experiments (Q4606435) (← links)
- Spatial Statistical Data Fusion for Remote Sensing Applications (Q4648542) (← links)
- Mission CO<sub>2</sub>ntrol: A Statistical Scientist's Role in Remote Sensing of Atmospheric Carbon Dioxide (Q4690936) (← links)
- Comment (Q4690942) (← links)
- Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach (Q4916458) (← links)
- A Resampling-Based Stochastic Approximation Method for Analysis of Large Geostatistical Data (Q4916950) (← links)
- Hierarchical Factor Models for Large Spatially Misaligned Data: A Low‐Rank Predictive Process Approach (Q4919554) (← links)
- Using temporal variability to improve spatial mapping with application to satellite data (Q4932237) (← links)
- A Unified Framework for Fitting Bayesian Semiparametric Models to Arbitrarily Censored Survival Data, Including Spatially Referenced Data (Q4962423) (← links)
- Fast Prediction of Deterministic Functions Using Sparse Grid Experimental Designs (Q4975627) (← links)
- A Distributed and Integrated Method of Moments for High-Dimensional Correlated Data Analysis (Q4999158) (← links)
- Modeling Nonstationary and Asymmetric Multivariate Spatial Covariances via Deformations (Q5041347) (← links)
- On Deconfounding Spatial Confounding in Linear Models (Q5050818) (← links)
- (Q5054610) (← links)
- Making Recursive Bayesian Inference Accessible (Q5056965) (← links)
- Partition-Based Nonstationary Covariance Estimation Using the Stochastic Score Approximation (Q5057228) (← links)
- A Fused Gaussian Process Model for Very Large Spatial Data (Q5065995) (← links)
- Nonstationary Modeling With Sparsity for Spatial Data via the Basis Graphical Lasso (Q5066402) (← links)
- An Approach to Incorporate Subsampling Into a Generic Bayesian Hierarchical Model (Q5066475) (← links)
- Matrix Autoregressive Spatio-Temporal Models (Q5066496) (← links)
- Gaussian Process Prediction using Design-Based Subsampling (Q5066795) (← links)
- Data generation for axially symmetric processes on the sphere (Q5082648) (← links)
- Areal prediction of survey data using Bayesian spatial generalised linear models (Q5083913) (← links)
- A Monte Carlo approach to quantifying discrepancies between intractable posterior distributions (Q5106878) (← links)
- Threshold knot selection for large-scale spatial models with applications to the<i>Deepwater Horizon</i>disaster (Q5107447) (← links)
- Bayesian Hierarchical Models With Conjugate Full-Conditional Distributions for Dependent Data From the Natural Exponential Family (Q5146051) (← links)
- (Q5149246) (← links)
- Moving Least Squares Regression for High-Dimensional Stochastic Simulation Metamodeling (Q5270669) (← links)
- Spline-Based Emulators for Radiative Shock Experiments With Measurement Error (Q5327264) (← links)
- Estimating Spatial Changes Over Time of Arctic Sea Ice using Hidden 2×2 Tables (Q5377196) (← links)
- Spatio‐temporal models for big multinomial data using the conditional multivariate logit‐beta distribution (Q5377202) (← links)
