Flexible factor model for handling missing data in supervised learning

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Authors

Bekker, Andriette, 1958-
Hashemi, Farzane
Arashi, Mohammad

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Publisher

Springer

Abstract

This 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.

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Keywords

Automobile dataset, Asymmetry, ECME algorithm, Expectation conditional maximization either (ECME), Factor analysis model, Heavy tails, Incomplete data, Liver disorders dataset

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Citation

Bekker, 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.