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.
- Spatio‐temporal smoothing and EM estimation for massive remote‐sensing data sets (Q5495689) (← links)
- Estimation and selection for spatial confounding regression models (Q5875272) (← links)
- Statistical Modeling for Spatio-Temporal Data From Stochastic Convection-Diffusion Processes (Q5881150) (← links)
- Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains (Q5885120) (← links)
- An adaptive spatial model for precipitation data from multiple satellites over large regions (Q5962747) (← links)
- Fast matrix computations for functional additive models (Q5963540) (← links)
- Comments on: process modeling for slope and aspect with application to elevation data maps (Q5970958) (← links)
- A general framework for Vecchia approximations of Gaussian processes (Q6032766) (← links)
- Distributional validation of precipitation data products with spatially varying mixture models (Q6045985) (← links)
- Distributed nearest-neighbor Gaussian processes (Q6049859) (← links)
- Bayesian latent variable co-kriging model in remote sensing for quality flagged observations (Q6050918) (← links)
- Penalized model-based clustering of complex functional data (Q6063147) (← links)
- Bayesian nonstationary spatial modeling for very large datasets (Q6069068) (← links)
- On detecting non‐monotonic trends in environmental time series: a fusion of local regression and bootstrap (Q6069070) (← links)
- Constructing valid spatial processes on the sphere using kernel convolutions (Q6069106) (← links)
- A stabilized and versatile spatial prediction method for geostatistical models (Q6069118) (← links)
- Modeling Nonstationarity in Space and Time (Q6079971) (← links)
- Modeling Temporally Evolving and Spatially Globally Dependent Data (Q6086581) (← links)
- A Bayesian hierarchical model for forecasting intermountain snow dynamics (Q6090040) (← links)
- A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes (Q6100557) (← links)
- Latent multivariate log-gamma models for high-dimensional multitype responses with application to daily fine particulate matter and mortality counts (Q6104100) (← links)
- Spatial change of support models for differentially private decennial census counts of persons by detailed race and ethnicity (Q6106267) (← links)
- Linear-Cost Covariance Functions for Gaussian Random Fields (Q6107197) (← links)
- Deep Compositional Spatial Models (Q6110701) (← links)
- Distributed Bayesian inference in massive spatial data (Q6111472) (← links)
- Joint Bayesian analysis of multiple response-types using the hierarchical generalized transformation model (Q6121614) (← links)
- Bayesian nonstationary and nonparametric covariance estimation for large spatial data (with discussion) (Q6121621) (← links)
- Gaussian orthogonal latent factor processes for large incomplete matrices of correlated data (Q6121983) (← links)
- Calibration of spatiotemporal forecasts from citizen science urban air pollution data with sparse recurrent neural networks (Q6138472) (← links)
- Confidence regions for the level curves of spatial data (Q6139090) (← links)
- Restricted spatial regression in practice: geostatistical models, confounding, and robustness under model misspecification (Q6139143) (← links)
- Assessing the estimation of nearly singular covariance matrices for modeling spatial variables (Q6144425) (← links)
- Scalable computations for nonstationary Gaussian processes (Q6173565) (← links)
- Scalable Physics-Based Maximum Likelihood Estimation Using Hierarchical Matrices (Q6177922) (← links)
- Assessing fit in Bayesian models for spatial processes (Q6179536) (← links)
- Credible regions for exceedance sets of geostatistical data (Q6179607) (← links)
- Practical likelihood analysis for spatial generalized linear mixed models (Q6179616) (← links)
- DeepKriging: Spatially Dependent Deep Neural Networks for Spatial Prediction (Q6185130) (← links)
- Global–local shrinkage multivariate logit-beta priors for multiple response-type data (Q6494412) (← links)
- Modelling space-time varying ENSO teleconnections to droughts in North America (Q6538495) (← links)
- Spatial data fusion for large non-Gaussian remote sensing datasets (Q6540535) (← links)
- Efficient inference of generalized spatial fusion models with flexible specification (Q6541490) (← links)
- Some enhancements to DeepKriging (Q6548800) (← 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)
- Leveraging the nugget parameter for efficient Gaussian process modeling (Q6569246) (← links)
- The Matérn model: a journey through statistics, numerical analysis and machine learning (Q6579154) (← links)
- A distance metric-based space-filling subsampling method for nonparametric models (Q6595793) (← links)
- Direct Bayesian linear regression for distribution-valued covariates (Q6597261) (← links)
- A variational inference-based heteroscedastic Gaussian process approach for simulation metamodeling (Q6600044) (← links)
- The how and why of Bayesian nonparametric causal inference (Q6601995) (← links)
