Bayesian Non-Parametric Factor Analysis for Longitudinal Spatial Surfaces

Bayesian inference for longitudinal data with non-parametric treatment effects.

We consider inference for longitudinal data based on mixed-effects models with a non-parametric Bayesian prior on the treatment effect. The proposed non-parametric Bayesian prior is a random partition model with a regression on patient-specific covariates. The main feature and motivation for the proposed model is the use of covariates with a mix of different data formats and possibly high-order...

Bayesian Correlated Factor Analysis for Spatial Data

A hierarchical Bayesian factor model for multivariate spatially correlated data is proposed. The idea behind the proposed method is to search factor scores incorporating a dependence due to a geographical structure. The great exibility of the Bayesian approach bears directly on the problem of parameter identi cation in factor analysis and furthermore on the inclusion of our prior opinion about ...

Stochastic Non-Parametric Frontier Analysis

In this paper we develop an approach that synthesizes the best features of the two main methods in the estimation of production efficiency. Specically, our approach first allows for statistical noise, similar to Stochastic frontier analysis, and second, it allows modeling multiple-inputs-multiple-outputs technologies without imposing parametric assumptions on production relationship, similar to...

Bayesian, and non-Bayesian, Cause-Specific Competing-Risk Analysis for Parametric and Non-Parametric Survival Functions: The R Package CFC

The R package CFC performs cause-specific, competing-risk survival analysis by computing cumulative incidence functions from unadjusted, cause-specific survival functions. A high-level API in CFC enables end-to-end survival and competing-risk analysis, using a single-line function call, based on the parametric survival regression models in survival package. A low-level API allows users to achie...

Bayesian Prediction in Spatial Data Analysis