Bekker, Andriette, 1958-Hashemi, FarzaneArashi, Mohammad2023-06-262023-06Bekker, A., Hashemi, F. & Arashi, M. Flexible Factor Model for Handling Missing Data in Supervised Learning. Communications in Mathematics and Statistics 11, 477–501 (2023). https://doi.org/10.1007/s40304-021-00260-9.2194-6701 (print)2194-671X (online)10.1007/s40304-021-00260-9http://hdl.handle.net/2263/91198This paper presents an extension of the factor analysis model based on the normal mean–variance mixture of the Birnbaum–Saunders in the presence of nonresponses and missing data. This model can be used as a powerful tool to model non-normal features observed from data such as strongly skewed and heavy-tailed noises. Missing data may occur due to operator error or incomplete data capturing therefore cannot be ignored in factor analysis modeling. We implement an EM-type algorithm for maximum likelihood estimation and propose single imputation of possible missing values under a missing at random mechanism. The potential and applicability of our proposed method are illustrated through analyzing both simulated and real datasets.en© School of Mathematical Sciences, University of Science and Technology of China and Springer-Verlag GmbH Germany, part of Springer Nature 2022. The original publication is available at : https://link.springer.com/journal/40304.Automobile datasetAsymmetryECME algorithmExpectation conditional maximization either (ECME)Factor analysis modelHeavy tailsIncomplete dataLiver disorders datasetPostprint Article