Handling datasets with partially observed variables presents significant challenges, particularly when the available information is minimal or nearly absent. Classical imputation methods can introduce bias, as leveraging the observed portion of the data may lead to an overestimation of the less frequent group in the population. In this work, we propose an indirect approach that exploits the partial information present in the data while accounting for the clustered structure induced by the unobserved variable. A simulation study and an educational case study complete the proposed work.
Nicolussi, F., Masci, C., Bertarelli, G., Mecatti, F. (2025). Learning Procedure for Partially Observed Variables. In E. di Bella, V. Gioia, C. Lagazio, S. Zaccarin (a cura di), Statistics for Innovation IV SIS 2025, Short Papers, Contributed Sessions 3 (pp. 108-113). Springer [10.1007/978-3-031-96033-8_19].
Learning Procedure for Partially Observed Variables
Mecatti, F
2025
Abstract
Handling datasets with partially observed variables presents significant challenges, particularly when the available information is minimal or nearly absent. Classical imputation methods can introduce bias, as leveraging the observed portion of the data may lead to an overestimation of the less frequent group in the population. In this work, we propose an indirect approach that exploits the partial information present in the data while accounting for the clustered structure induced by the unobserved variable. A simulation study and an educational case study complete the proposed work.
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