Variable selection in uncertain regression analysis with imprecise observations

Variable selection is crucial in order to better investigate relationships between variables in regression analysis. However, sometimes data are collected in an imprecise way and can not be described by random variables. As a result, classical variable selection methods are invalid. Characterizing these imprecise observations as uncertain variables, this paper presents the uncertain lasso estimate and the de-biased uncertain lasso estimate to select variables and estimate unknown parameters, respectively. Moreover, a way to choose the tuning parameter using cross-validation is suggested. Finally, numerical examples are documented to show our methods in detail.

Automated summary

Variable selection is important in regression analysis, but can be challenging when data is imprecise. This paper introduces a method using uncertain variables for variable selection and parameter estimation, along with a proposed approach for tuning parameter selection through cross-validation. Numerical examples demonstrate the effectiveness of the methods presented.