Missing binary predictors are common in reliability, quality control, and industrial decision systems, yet imputation methods are often chosen by convenience rather than evidence. We conduct a Monte Carlo study comparing mode substitution, sequential hot‐deck, missForest, MICE, and KNN with three neighbourhood sizes under MCAR, MAR, and MNAR missingness, across missingness rates from 5% to 50% and two predictor‐dependence structures. Performance is evaluated on three targets: exact recovery of missing binary cells, recovery of logistic‐regression coefficients, and downstream classification using logistic regression, naive Bayes, support vector machines, and random forests. The results reveal a clear trade‐off. KNN is strongest for exact cell recovery under MCAR and MAR, whereas missForest performs best under MNAR. MICE is the most reliable choice for downstream predictive performance across learners and missingness mechanisms. By contrast, mode imputation and sequential hot‐deck achieve the best coefficient recovery. The main implication is operational: in binary‐data environments, imputation should be chosen to match the analytical objective–reconstruction, inference, or prediction–because no single method dominates all targets simultaneously.
This paper compares several approaches for handling MNAR data in linear regression when missingness depends on both partially observed outcomes and predictors and concludes that not-at-random fully conditional specification was straightforward to implement and yielded coverage close to the nominal level in most scenari...
T. Gorbach, Tim P. Morris, James E. Carpenter· Statistical Methods in Medic...· 0 citations
The results indicate that machine learning algorithms, particularly missForest and KNN, consistently provide high accuracy by modeling complex and nonlinear relationships and recommend advanced machine learning algorithms to ensure unbiased inference, while cautioning against simple imputation techniques that may intro...
NoorAldeen Salam Taher, N. J. Mohammed· ITEGAM- Journal of Engineeri...· 0 citations
This study systematically compared statistical and machine learning-based imputation methods using two publicly available breast cancer datasets representing complementary clinical settings to highlight the importance of considering dataset characteristics, missing-data mechanisms, and the intended analytical objective...
Nyatuga Gideon Nyakundi, John Ndiritu, Ivivi J. Mwaniki et al.· AppliedMath· 0 citations
Missing data are common in environmental mixture studies and can bias inference if not properly addressed. This study evaluates how different imputation strategies influence variable selection performance in six mixture modeling frameworks: Weighted Quantile Sum (WQS), Bayesian WQS (BWQS), Quantile g-computation (Q-gco...
Y. S. Boafo, S. Mostafa, E. Obeng-Gyasi· Data Science in Science· 0 citations
This work proposes the columnwise neural imputation (COLNI) algorithm, a artificial neural network-based imputation approach to impute missing ordinal responses in psychometric data, and evaluates its effectiveness in a multidimensional empirical setting.
For model comparison in random effects probit models with incompletely observed covariates, this paper develops a Bayesian data-augmentation workflow in which latent Gaussian responses, random effects, and missing covariate values are updated within a common augmented sampling scheme. Because specifying a fully param...
Michael Bergrab· Statistical Methods & Ap...· 0 citations
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