Publications (5)
spBayesSurv: Fitting Bayesian Spatial Survival Models Using R
Haiming Zhou, Timothy Hanson, Jiajia Zhang
Spatial survival analysis has received a great deal of attention over the last 20 years due to the important role that geographical information can play in predicting survival. Thi…
Modeling county level breast cancer survival data using a covariate-adjusted frailty proportional hazards model
Haiming Zhou, Timothy Hanson, Alejandro Jara +1
Understanding the factors that explain differences in survival times is an important issue for establishing policies to improve national health systems. Motivated by breast cancer…
Bayesian quantile additive regression trees
Bereket P. Kindo, Hao Wang, Timothy Hanson +1
Ensemble of regression trees have become popular statistical tools for the estimation of conditional mean given a set of predictors. However, quantile regression trees and their en…
Checking the Statistical Assumptions Underlying the Application of the Standard Deviation and RMS Error to Eye-Movement Time Series: A Comparison between Human and Artificial Eyes
Lee Friedman, Timothy Hanson, Hal S. Stern +1
Spatial precision is often measured using the standard deviation (SD) of the eye position signal or the RMS of the sample-to-sample differences (StoS) signal during fixation. As bo…
A unified framework for fitting Bayesian semiparametric models to arbitrarily censored survival data, including spatially-referenced data
Haiming Zhou, Timothy Hanson
A comprehensive, unified approach to modeling arbitrarily censored spatial survival data is presented for the three most commonly-used semiparametric models: proportional hazards,…