tBayes-MICE: A Bayesian Approach to Multiple Imputation for Time Series Data
Time-series analysis is often affected by missing data, a common problem across several fields, including healthcare and environmental monitoring. Multiple Imputation by Chained Equations (MICE) has been prominent for imputing missing values through "fully conditional specification". We extend MICE using the Bayesian framework (tBayes-MICE), utilising Bayesian inference to impute missing values via Markov Chain Monte Carlo (MCMC) sampling to account for uncertainty in MICE model parameters and imputed values. We also include temporally informed initialisation and time-lagged features in the model to respect the sequential nature of time-series data. We evaluate the tBayes-MICE method usin...
| Authors | Amuche Ibenegbu, Pierre Lafaye de Micheaux, Rohitash Chandra |
|---|---|
| Primary category | stat.ML |
| Published | 28 March 2026 |
| Last updated at source | 9 April 2026 |
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