Estimating the Risk of Reoffending by using Exponential Mixture Models
研究用指数混合模型重新分析假释犯再犯数据，通过明确考虑犯罪到定罪的时间延迟，使风险评分能基于更短随访期数据构建，降低成本并提高时效性。
Summary In deciding whether to release a prisoner on parole, the Parole Board is provided with a statistical score which estimates the chance that the prisoner will reoffend within the period of time that he or she would otherwise be in prison. This score is based on a survival analysis of data on a sample of releases from long-term prison sentences. To capture most reoffences which occur within 2 years of release, follow-up must continue for at least 3 years to allow for the delay between offence and conviction. We reanalyse the data by using a model which explicitly allows for this delay. The new analysis can be applied to data with a substantially shorter length of follow-up. This means that risk scores can be constructed from more up-to-date data and at less cost.