Bayesian Extended Redundancy Analysis: A Bayesian Approach to Component-based Regression with Dimension Reduction. (2nd January 2020)

• Record Type: Journal Article
• Authors: Choi, Ji Yeh; Kyung, Minjung; Hwang, Heungsun; Park, Ju-Hyun
• Is Part Of: Multivariate behavioral research. Volume 55:Number 1(2020)
• Page Start: 30
• Page End: 48
• Publication Date: 2020-01-02
• Subjects: Bayesian methodology -- extended redundancy analysis -- missing data -- multiple imputation -- power prior distribution; Psychometrics -- Periodicals; Psychology, Experimental -- Periodicals; Psychology, Experimental; Psychometrics; Periodicals; 150.15195
• DOI: 10.1080/00273171.2019.1598837
• Languages: English
• ISSNs: 0027-3171

Abstract

Abstract: Extended redundancy analysis (ERA) combines linear regression with dimension reduction to explore the directional relationships between multiple sets of predictors and outcome variables in a parsimonious manner. It aims to extract a component from each set of predictors in such a way that it accounts for the maximum variance of outcome variables. In this article, we extend ERA into the Bayesian framework, called Bayesian ERA (BERA). The advantages of BERA are threefold. First, BERA enables to make statistical inferences based on samples drawn from the joint posterior distribution of parameters obtained from a Markov chain Monte Carlo algorithm. As such, it does not necessitate any resampling method, which is on the other hand required for (frequentist's) ordinary ERA to test the statistical significance of parameter estimates. Second, it formally incorporates relevant information obtained from previous research into analyses by specifying informative power prior distributions. Third, BERA handles missing data by implementing multiple imputation using a Markov Chain Monte Carlo algorithm, avoiding the potential bias of parameter estimates due to missing data. We assess the performance of BERA through simulation studies and apply BERA to real data regarding academic achievement.