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Bayesian Gaussiandistributionalregression models formore efficient normestimation Lieke VonckenThomas KneibCasper J. Albers2020 год

Bayesian Gaussian distributional regression models for more efficient norm estimation
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Аннотация: 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 and sample size. In our simulated conditions, using a fixed effects prior resulted in more efficient norm estimation than a weakly informative prior as long as the prior misspecification was not age dependent. With the proposed method and reasonable prior information, the same norm precision can be achieved with a smaller normative sample, at least in empirical problems similar to our simulated conditions. This may help test developers to achieve cost‐efficient high‐quality norms. The method is illustrated using empirical normative data from the IDS‐2 intelligence test.
Год издания: 2020
Авторы: Lieke Voncken, Thomas Kneib, Casper J. Albers, Nikolaus Umlauf, Marieke E. Timmerman
Издательство: Wiley
Источник: British Journal of Mathematical and Statistical Psychology
Ключевые слова: Statistical Methods and Inference, Statistical Methods and Bayesian Inference, Insurance, Mortality, Demography, Risk Management
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