고위험 음주 자료에 대한 베이지안 비대칭 로짓 모형 분석

초록

In the data on the causes and characteristics of high-risk drinking incidents conducted by the Korea Centers for Disease Control and Prevention (KCDC), high-risk drinking variable has features of unbalanced binary data that are extremely skewed. In this case, symmetric link function models including the logit model and the probit model may yield biased estimates of the parameters. To figure out this issue, we used a skewed logit model, which is one of the skewed link models to analyze such unbalanced binary data based on Bayesian inference methods. The skewed link model is a generalized model that includes the symmetric and asymmetric link function models, and has the advantage of ensuring the propriety of the posterior distribution when using an improper noninformative prior distribution in Bayesian inference. The analysis of the model on high-risk drinking data showed that the skewed logit model is more appropriate for explaining asymmetric binary data than the other comparing models.

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