Efficient inference for spatial extreme value processes associated to log-Gaussian random functions
نویسندگان
چکیده
منابع مشابه
Going off grid: Computationally efficient inference for log-Gaussian Cox processes
In this paper we introduce a new method for performing computational inference on log-Gaussian Cox processes (LGCP). Contrary to current practice, we do not approximate by a counting process on a partition of the domain, but rather attack the point process likelihood directly. In order to do this, we use the continuously specified Markovian random fields introduced by Lindgren et al. (2011). Th...
متن کاملSparse Log Gaussian Processes via MCMC for Spatial Epidemiology
In this work a fully independent training conditional (FITC) sparse approximation is used to speed up GP computations in the study of the spatial variations in relative mortality risk in a point referenced health-care data. The sampling of the latent values is sped up with transformations taking into account the approximate conditional posterior precision. Log Gaussian processes (LGP) are an at...
متن کاملModerate deviations for log-like functions of stationary Gaussian processes
A moderate deviation principle for nonlinear functions of Gaussian processes is established. The nonlinear functions need not be locally bounded. Especially, the logarithm is allowed. (Thus, small deviations of the process are relevant.) Both discrete and continuous time is treated. An integrable power-like decay of the correlation function is assumed.
متن کاملExtreme value distributions for nonlinear transformations of vector Gaussian processes
Approximations are developed for the marginal and joint probability distributions for the extreme values, associated with a vector of nonGaussian random processes. The component non-Gaussian processes are obtained as nonlinear transformations of a vector of stationary, mutually correlated, Gaussian random processes and are thus, mutually dependent. The multivariate counting process, associated ...
متن کاملlgcp: Inference with Spatial and Spatio-Temporal Log-Gaussian Cox Processes in R
This paper introduces an R package for spatial and spatio-temporal prediction and forecasting for log-Gaussian Cox processes. The main computational tool for these models is Markov chain Monte Carlo (MCMC) and the new package, lgcp, therefore also provides an extensible suite of functions for implementing MCMC algorithms for processes of this type. The modelling framework and details of inferen...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Biometrika
سال: 2013
ISSN: 0006-3444,1464-3510
DOI: 10.1093/biomet/ast042