Logistic Regression Through the Veil of Imprecise Data
This addresses the issue of handling uncertainties in datasets for statisticians and data scientists, though it appears incremental as it builds on existing logistic regression methods.
The paper tackles the problem of logistic regression with uncertain data by introducing an imprecise model that includes uncertainties through intervals, resulting in a method that expresses epistemic uncertainty removed by traditional approaches.
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.