Pages that link to "Item:Q1399278"
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The following pages link to Modeling and prediction for multivariate spatial factor analysis (Q1399278):
Displaying 17 items.
- Capturing multivariate spatial dependence: model, estimate and then predict (Q254433) (← links)
- Bayesian factor analysis for spatially correlated data: application to cancer incidence data in Scotland (Q257469) (← links)
- An overview of approaches to the analysis and modelling of multivariate geostatistical data (Q500627) (← links)
- A spatial analysis of multivariate output from regional climate models (Q542461) (← links)
- Spatial analysis of auto-multivariate lattice data (Q657081) (← links)
- Generation of prediction optimal projection on latent factors by a stochastic search algorithm (Q957009) (← links)
- A latent factor model for spatial data with informative missingness (Q977647) (← links)
- Extraction of spatial features using factor methods, illustrated on stream sediment data (Q995829) (← links)
- Generalized shifted-factor analysis method for multivariate geo-referenced data (Q1863213) (← links)
- Multivariate spatial regression models (Q1888332) (← links)
- A Hierarchical Bayesian Model for Spatial Prediction of Multivariate Non-Gaussian Random Fields (Q3008861) (← links)
- Latent Variable Modelling: A Survey* (Q3608238) (← links)
- Hierarchical Factor Models for Large Spatially Misaligned Data: A Low‐Rank Predictive Process Approach (Q4919554) (← links)
- Modelling multivariate disease rates with a latent structure mixture model (Q4970583) (← links)
- Generalized common spatial factor model (Q5701221) (← links)
- Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains (Q5885120) (← links)
- A Bayesian spatial factor analysis approach for combining climate model ensembles (Q6139089) (← links)
