基于生存概念的刑事犯罪被告人供述过程风险建模

Olha Kovalchuk
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引用次数: 4

摘要

在统计生存分析的基础上,对数据不完全条件下的刑事犯罪认罪风险进行了评估和预测。构建了风险函数来估计嫌疑人在审判的特定阶段(时间段)招供的概率。在刑事诉讼中,Kaplan-Meier模型用于计算审判结束后获得供词证据的机会。两组被告认罪决定的差异:在一个人和一组人的犯罪行为中进行调查。构建Cox回归模型,建立被告人供述的审前侦查阶段与侦查时间、起诉方式之间的联系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling the risks of the confession process of the accused of criminal offenses based on survival concept
Based on statistical survival analysis, the assessment and forecasting of the risks of pleading guilty to criminal offenses in conditions of incomplete data are carried out. Risk function is constructed to estimate the probability of confession of suspects at certain stages (time periods) of the trial. The Kaplan-Meier model is applied to calculate the chances of obtaining confession evidence after the end of the trial in criminal proceedings. Differences in the decision to admit guilt for two groups of defendants: in the commission of a criminal offense by one person and a group of persons are investigated. Cox regression model is constructed to establish the interconnection between the stages of the pre-trial investigation, at which the accused gives confessions, with the duration of the investigation and the method of prosecution.
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