Pages that link to "Item:Q3459928"
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The following pages link to R‐vine models for spatial time series with an application to daily mean temperature (Q3459928):
Displaying 17 items.
- Variational inference for high dimensional structured factor copulas (Q830616) (← links)
- Sequential truncation of \(R\)-vine copula mixture model for high-dimensional datasets (Q1980359) (← links)
- Modelling mortality dependence: an application of dynamic vine copula (Q2038244) (← links)
- A mixture of regular vines for multiple dependencies (Q2039146) (← links)
- Modeling spatial tail dependence with Cauchy convolution processes (Q2106793) (← links)
- Spatial pair-copula model of grade for an anisotropic gold deposit (Q2325285) (← links)
- Spatial composite likelihood inference using local C-vines (Q2350040) (← links)
- Forecasting time series with multivariate copulas (Q2351202) (← links)
- Intermuscular coupling network analysis of upper limbs based on R-vine copula transfer entropy (Q2688610) (← links)
- Generalized Additive Models for Pair-Copula Constructions (Q3391152) (← links)
- Copula diagnostics for asymmetries and conditional dependence (Q5037090) (← links)
- Knowledge Learning of Insurance Risks Using Dependence Models (Q5085485) (← links)
- Factor Copula Approaches for Assessing Spatially Dependent High-Dimensional Risks (Q5379211) (← links)
- A Dynamic Stochastic Integrated Climate–Economic Spatiotemporal Model for Agricultural Insurance Products (Q6549252) (← links)
- Modeling Multivariate Time Series With Copula-Linked Univariate D-Vines (Q6620894) (← links)
- Analysis of paediatric visual acuity using Bayesian copula models with sinh-arcsinh marginal densities (Q6624716) (← links)
- A Composite Likelihood-Based Approach for Change-Point Detection in Spatio-Temporal Processes (Q6651415) (← links)
