Multivariate meta-regression models are commonly used in settings where the response variable is naturally multidimensional. Such settings are common in cardiovascular and diabetes studies where the ...

We consider Bayesian inferences on a type of multivariate median and the multivariate quantile functionals of a joint distribution using a Dirichlet process prior. Unlike univariate quantiles, the ...

Title: Bayesian Multivariate Regression and Path Analysis Models with Variable Selection for high-dimensional and longitudinal Genetic Epidemiology Abstract: We present Bayesian seemingly unrelated ...

The goal of a machine learning regression problem is to predict a single numeric value. There are roughly a dozen different regression techniques such as basic linear regression, k-nearest neighbors ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the naive Bayes regression technique, where the goal is to predict a single numeric value. Compared to other ...

Introduction: The diagnosis of chronic obstructive pulmonary disease (COPD) is based on spirometry tests, which are difficult to perform in some populations. Objectives: We aimed to construct a risk ...

We adapt a semi-Bayesian hierarchical modeling framework to jointly characterize the space–time variability of seasonal precipitation totals and precipitation extremes across the Northern Great Plains ...