New method corrects bias in regression models, improving accuracy of predictions.
The article presents a method to fix bias in statistical models caused by non-random sample selection. By using a copula-based approach, the researchers corrected this bias in Bayesian regression models, allowing for more flexible and accurate predictions. The new method considers various types of effects, not just linear ones, and models distributional parameters as functions of the data. Through empirical evaluation, the researchers showed that their approach outperformed a traditional method in a study on psychological judge-advisor data.
