Bayesian Spatial Modeling of Precipitation Data

Bayesian Spatial Modeling of Extreme Precipitation Return Levels

Quantification of precipitation extremes is important for flood planning purposes, and a common measure of extreme events is the r-year return-level. We present a method for producing maps of precipitation return levels and uncertainty measures and apply to a Colorado region. Separate hierarchical models are constructed for the intensity and the frequency of extreme precipitation events. For in...

Efficient Bayesian Hierarchical Modeling of Spatial Precipitation Extremes

An efficient Bayesian hierarchical model for spatial extremes on a large domain is proposed. In the data layer a Gaussian elliptical copula having generalized extreme value (GEV) marginals is applied. Spatial dependence in the GEV parameters are captured with a latent spatial regression. Using a composite likelihood approach and a method for incorporating stations with missing data, we are able...

Bayesian Prediction in Spatial Data Analysis

spatial modeling of summer precipitation in North-west of Iran

In the present study, the main aim was the spatial evaluation summer rainfall of northwest of Iran based on30 stations in northwest of Iran during 30 years of statistical period (1985-2014). An attempt, using geo-statistical modeling by ordinary least squares (OLS) and geographically weighted regression (GWR) procedures, was also made. The results represented that the GWR model with higher S2, ...

Spatial Modeling of Censored Survival Data

An important issue in survival data analysis is the identification of risk factors. Some of these factors are identifiable and explainable by presence of some covariates in the Cox proportional hazard model, while the others are unidentifiable or even immeasurable. Spatial correlation of censored survival data is one of these sources that are rarely considered in the literatures. In this paper,...