Bayesian inference for big spatial data using non-stationary spectral simulation

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...

Predictive Inference for Big, Spatial, Non-Gaussian Data: MODIS Cloud Data and its Change-of-Support

Remote sensing of the earth with satellites yields datasets that can be massive in size, nonstationary in space, and non-Gaussian in distribution. To overcome computational challenges, we use the reduced-rank spatial random effects (SRE) model in a statistical analysis of cloud-mask data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board NASA's Terra satellite...

Bayesian Clustering of Non-stationary Data

Non-stationary data clustering is a hard, ill-posed problem, which is nevertheless unavoidable in several scientific fields. A representative example is the problem of Spike Sorting, which involves clustering spike trains recorded from the brain by a micro-electrode, according to source neuron. It is a complicated problem which requires a lot of human labor, partly due to the non-stationary nat...

Bayesian Prediction in Spatial Data Analysis

Bayesian Analysis of Censored Spatial Data Based on a Non-Gaussian Model

Abstract: In this paper, we suggest using a skew Gaussian-log Gaussian model for the analysis of spatial censored data from a Bayesian point of view. This approach furnishes an extension of the skew log Gaussian model to accommodate to both skewness and heavy tails and also censored data. All of the characteristics mentioned are three pervasive features of spatial data. We utilize data augme...