Bayesian Analysis of Survival Data with Spatial Correlation

Bayesian Prediction in Spatial Data Analysis

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,...

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...

Semiparametric Bayesian Analysis of Survival Data

This review paper investigates the potential of the semiparametric Bayes methods for the analysis of survival data. The nonparametric part of every semiparametric model is assumed to be a realization of a stochastic process. The parametric part, which may include a regression parameter or a parameter quantifying the heterogeneity of a population, is assumed to have a prior distribution with pos...

Bayesian Correlation Analysis for Sequence Count Data

Evaluating the similarity of different measured variables is a fundamental task of statistics, and a key part of many bioinformatics algorithms. Here we propose a Bayesian scheme for estimating the correlation between different entities' measurements based on high-throughput sequencing data. These entities could be different genes or miRNAs whose expression is measured by RNA-seq, different tra...

Bayesian Analysis of fMRI data with Spatial Priors

Functional Magnetic Resonance Imaging (fMRI) using Blood Oxygen Level Dependent (BOLD) contrast is an established method for making inferences about regionally specific activations in the human brain [7]. From measurements of changes in blood oxygenation one can use various statistical models, such as the General Linear Model (GLM) [8], to make inferences about task-specific changes in underlyi...