deckimputation

Deckimputation is a term used to describe a family of data imputation techniques that organize candidate imputations for missing values as a “deck” from which values are drawn or selected. The central idea is to replace missing entries by sampling from or selecting among a curated set of plausible values, rather than applying a single deterministic replacement.

In practice, deckimputation builds a deck of candidate imputations for each missing entry. Candidates may come from observed data in similar rows or columns, from a learned predictive model, or from inferred distributions. Imputation proceeds by drawing from the deck, either once to produce a single completed dataset or repeatedly (as in multiple imputation) to create several datasets that reflect uncertainty about the missing values. Variants may combine deck-based sampling with standard imputation ideas such as Bayesian inference, k-nearest neighbors, or matrix factorization.

Deckimputation is used across domains where missing data are common, including survey research, finance, and bioinformatics. It aims to balance fidelity to the observed data distribution with the need to preserve uncertainty about missing values. Advantages can include flexibility in representing multiple plausible values and robustness to outliers when the deck is constructed carefully. Limitations include sensitivity to how the deck is built, potential computational demands, and the risk of bias if the candidate pool is unrepresentative of the true missing-data mechanism.

See also: data imputation, multiple imputation, k-nearest neighbors imputation, Bayesian imputation, matrix completion.