Remote effects spatial process models for modeling teleconnections

Properties of Spatial Cox Process Models

Probabilistic properties of Cox processes of relevance for statistical modeling and inference are studied. Particularly, we study the most important classes of Cox processes, including log Gaussian Cox processes, shot noise Cox processes, and permanent Cox processes. We consider moment properties and point process operations such as thinning, displacements, and superpositioning. We also discuss...

Multivariate Spatial Process Models

Continuous Spatial Process Models for Spatial Extreme Values

We propose a hierarchical modeling approach for explaining a collection of point-referenced extreme values. In particular, annual maxima over space and time are assumed to follow Generalized Extreme Value (GEV) distributions, with parameters μ , σ , and ξ specified in the latent stage to reflect underlying spatio-temporal structure. The novelty here is that we relax the conditionally independen...

Generalized Spatial Dirichlet Process Models

By JASON A. DUAN Institute of Statistics and Decision Sciences at Duke University, Durham, North Carolina, 27708-0251, U.S.A. [email protected] MICHELE GUINDANI Istituto di Metodi Quantitativi, Università Bocconi, 20136, Milano, Italy [email protected] and ALAN E. GELFAND Institute of Statistics and Decision Sciences at Duke University, Durham, North Carolina, 27708-0251, U.S.A. ...

Bayesian Inference for Spatial Beta Generalized Linear Mixed Models

In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...