Pages that link to "Item:Q5214554"
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The following pages link to Bayesian modeling of air pollution extremes using nested multivariate max‐stable processes (Q5214554):
Displaying 15 items.
- Estimation and uncertainty quantification for extreme quantile regions (Q73765) (← links)
- Editorial: EVA 2019 data competition on spatio-temporal prediction of Red Sea surface temperature extremes (Q2028570) (← links)
- Erratum to: ``Estimation and uncertainty quantification for extreme quantile regions'' (Q2028593) (← links)
- Modeling spatial tail dependence with Cauchy convolution processes (Q2106793) (← links)
- Approximate Bayesian inference for analysis of spatiotemporal flood frequency data (Q2154186) (← links)
- Bayesian Model Averaging Over Tree-based Dependence Structures for Multivariate Extremes (Q3391465) (← links)
- Bayesian Spatial Clustering of Extremal Behavior for Hydrological Variables (Q5066419) (← links)
- Local Likelihood Estimation of Complex Tail Dependence Structures, Applied to U.S. Precipitation Extremes (Q5120643) (← links)
- A Bayesian Kriged Kalman Model for Short-Term Forecasting of Air Pollution Levels (Q5757753) (← links)
- A Bayesian multivariate receptor model for estimating source contributions to particulate matter pollution using national databases (Q6139091) (← links)
- Advances in statistical modeling of spatial extremes (Q6602343) (← links)
- A multivariate spatial skew-\(t\) process for joint modeling of extreme precipitation indexes (Q6626136) (← links)
- Spatial hierarchical modeling of threshold exceedances using rate mixtures (Q6626383) (← links)
- Practical strategies for generalized extreme value-based regression models for extremes (Q6626492) (← links)
- Partial Tail-Correlation Coefficient Applied to Extremal-Network Learning (Q6637459) (← links)
