Bayesian Gaussian distributional regression models for more efficient norm estimation.

ABSTRACT: A test score on a psychological test is usually expressed as a normed score, representing its position relative to test scores in a reference population. These typically depend on predictor(s) such as age. The test score distribution conditional on predictors is estimated using regression, which may need large normative samples to estimate the relationships between the predictor(s) and the distribution characteristics properly. In this study, we examine to what extent this burden can be alleviated by using prior information in the estimation of new norms with Bayesian Gaussian distributional regression. In a simulation study, we investigate to what extent this norm estimation is more efficient and how robust it is to prior model deviations. We varied the prior type, prior misspecification

SUBMITTER: Voncken L 

PROVIDER: S-EPMC7891623 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature