Logistic regression is an important statistical tool for assessing the probability of an outcome based upon some predictive variables. Standard methods can only deal with precisely known data, however many datasets have uncertainties which traditional methods either reduce to a single point or completely disregarded. In this paper we show that it is possible to include these uncertainties by considering an imprecise logistic regression model using the set of possible models that can be obtained from values from within the intervals. This has the advantage of clearly expressing the epistemic uncertainty removed by traditional methods.

翻译：后勤回归是评估基于某些预测变量的结果概率的重要统计工具,标准方法只能处理精确已知的数据,但许多数据集具有不确定性,传统方法要么降低到一个点,要么完全忽略。在本文中,我们表明,通过考虑采用不精确的后勤回归模型,利用从间隔内从数值中获取的一套可能的模型,有可能纳入这些不确定性。这具有明确表达传统方法所消除的特征不确定性的优点。