Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component 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 Survival Data with Spatial Correlation

Often in practice the data on the mortality of a living unit correlation is due to the location of the observations in the study‎. ‎One of the most important issues in the analysis of survival data with spatial dependence‎, ‎is estimation of the parameters and prediction of the unknown values in known sites based on observations vector‎. ‎In this paper to analyze this type of survival‎, ‎Cox...

Bayesian Prediction in Spatial Data Analysis

Regularized Principal Component Analysis for Spatial Data

Abstract: In many atmospheric and earth sciences, it is of interest to identify dominant spatial patterns of variation based on data observed at p locations with n repeated measurements. While principal component analysis (PCA) is commonly applied to find the patterns, the eigenimages produced from PCA may be noisy or exhibit patterns that are not physically meaningful when p is large relative ...

Modeling Spatial Distribution of Intracellular Organelles by Molecular Motors