Nearest Neighbour Imputation

Nearest neighbour imputation addresses missing data through replacement by similar complete-case values. Distance metrics determine observation similarity. Single imputation introduces bias; multiple imputation accounts for uncertainty. Random selection among candidates preserves variability. Monotone missingness permits sequential imputation.

Missing data mechanism assessment determines imputation appropriateness. Regulatory acceptance requires missing data handling justification. Sensitivity analysis confirms robustness to imputation assumptions. Advanced statistical methods improve imputation accuracy. Post-analysis transparency clarifies missing data procedures.