Accession Number:

AD1076799

Title:

Bayesian Reduced-Rank Regression with Stan

Descriptive Note:

Technical Report,01 Sep 2017,30 Aug 2019

Corporate Author:

ARMY RESEARCH LAB ADELPHI MD Playa Vista United States

Report Date:

2019-07-01

Pagination or Media Count:

21.0

Abstract:

Reduced-rank regression enables characterizing the relationship between several predictors and outcome measures when their relationship can be accounted for with a relatively small number of latent dimensions. In contrast to full-rank multivariate regression, reduced-rank regression avoids estimating redundant regression coefficients and efficiently uncovers the underlying lower-dimensional latent variables that characterize the relationship between predictors and outcomes. Here, we report on an implementation of reduced-rank regression in a Bayesian framework using Markov Chain Monte Carlo, No- U-Turn Sampling as implemented in Stan, a popular open-source Bayesian inference engine. This implementation supports robust error modelling and calculation of posterior uncertainty intervals.

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE