Pages that link to "Item:Q1031769"
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The following pages link to A modeling approach for large spatial datasets (Q1031769):
Displaying 33 items.
- Comparing composite likelihood methods based on pairs for spatial Gaussian random fields (Q69394) (← links)
- An approximate likelihood function of spatial correlation parameters (Q287412) (← links)
- Asymptotic properties of multivariate tapering for estimation and prediction (Q290722) (← links)
- Global space-time models for climate ensembles (Q386745) (← links)
- Approximate Bayesian inference for large spatial datasets using predictive process models (Q434887) (← links)
- A simplified representation of the covariance structure of axially symmetric processes on the sphere (Q449401) (← links)
- 2010 Rietz lecture: When does the screening effect hold? (Q449966) (← links)
- Modeling complex spatial dependencies: low-rank spatially varying cross-covariances with application to soil nutrient data (Q486034) (← links)
- Efficient emulators of computer experiments using compactly supported correlation functions, with an application to cosmology (Q765992) (← links)
- Covariance approximation for large multivariate spatial data sets with an application to multiple climate model errors (Q765994) (← links)
- Krigings over space and time based on latent low-dimensional structures (Q829391) (← links)
- Hierarchical spatial models for predicting tree species assemblages across large domains (Q985023) (← links)
- Nonstationary covariance models for global data (Q999661) (← links)
- Modeling nonstationary covariance function with convolution on sphere (Q1658744) (← links)
- Fast and interactive editing tools for spatial models (Q1715422) (← links)
- Spatial regression with non-parametric modeling of Fourier coefficients (Q2151603) (← links)
- Multi-scale process modelling and distributed computation for spatial data (Q2209724) (← links)
- Combining heterogeneous spatial datasets with process-based spatial fusion models: a unifying framework (Q2242020) (← links)
- Reduced-rank spatio-temporal modeling of air pollution concentrations in the multi-ethnic study of atherosclerosis and air pollution (Q2258600) (← links)
- Improving crop model inference through Bayesian melding with spatially varying parameters (Q2261034) (← links)
- Towards a complete picture of stationary covariance functions on spheres cross time (Q2316610) (← links)
- Stochastic approximation of score functions for Gaussian processes (Q2443174) (← links)
- Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets (Q3195190) (← links)
- Fixed Rank Kriging for Very Large Spatial Data Sets (Q3631452) (← links)
- Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach (Q4916458) (← links)
- Spatio‐temporal smoothing and EM estimation for massive remote‐sensing data sets (Q5495689) (← links)
- Bayesian nonstationary spatial modeling for very large datasets (Q6069068) (← links)
- Linear-Cost Covariance Functions for Gaussian Random Fields (Q6107197) (← links)
- Efficient inference of generalized spatial fusion models with flexible specification (Q6541490) (← links)
- Nearest neighbors weighted composite likelihood based on pairs for (non-)Gaussian massive spatial data with an application to Tukey-\(hh\) random fields estimation (Q6554236) (← links)
- Nonseparable covariance models on circles cross time: a study of Mexico City ozone (Q6626087) (← links)
- A Scalable Gaussian Process for Large-Scale Periodic Data (Q6631142) (← links)
- Efficient Stochastic Generators with Spherical Harmonic Transformation for High-Resolution Global Climate Simulations from CESM2-LENS2 (Q6651353) (← links)
