预测失业青年的贝叶斯方法
A Bayesian Approach to Predicting Disengaged Youth
摘要：
本文提出了一种贝叶斯方法来预测和确定最影响个人的倾向，属于类别的非就业教育或培训（NEET）的因素。该方法将协变量分为两组：那些有可能被改变的干预策略和那些必须控制的结果。这种划分使我们能够开发模型，并确定控制协变量的重要因素，这对于希望确定潜在干预策略的临床医生和政策制定者是有用的。使用O'Dea（2014）获得的数据，我们将该方法的结果与O'Dea（2014）的结果以及使用Lamnisos（2009）的贝叶斯变量选择程序获得的结果进行了比较。我们发现，预测因素的相对重要性变化很大，这取决于控制协变量。在决定什么样的干预措施对防止年轻人成为NEET最有用时，这具有巨大的影响。
英文摘要：
This article presents a Bayesian approach for predicting and identifying the factors which most influence an individual's propensity to fall into the category of Not in Employment Education or Training (NEET). The approach partitions the covariates into two groups: those which have the potential to be changed as a result of an intervention strategy and those which must be controlled for. This partition allows us to develop models and identify important factors conditional on the control covariates, which is useful for clinicians and policy makers who wish to identify potential intervention strategies. Using the data obtained by O'Dea (2014) we compare the results from this approach with the results from O'Dea (2014) and with the results obtained using the Bayesian variable selection procedure of Lamnisos (2009) when the covariates are not partitioned. We find that the relative importance of predictive factors varies greatly depending upon the control covariates. This has enormous implications when deciding on what interventions are most useful to prevent young people from being NEET.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
