Repairedvary

Repairedvary is a term used in statistics to describe a family of methods designed to adjust an estimator of variance after data repair operations have altered the distribution of observations. The term lacks a single standardized definition and appears primarily in theoretical or methodological discussions rather than in formal textbooks.

Conceptually, repairedvary addresses bias in variance estimates that arises when data cleaning, imputation, censoring, or outlier handling modifies the observed data. A repairedvary procedure aims to recover the underlying population variability by incorporating the repair mechanism into the variance estimation rather than treating repair as noise.

In typical formulations, one specifies a repair model for how repaired data relate to the original measurements. The corrected variance estimator can be expressed as Var_hat_repaired = Var_hat_observed + Delta, where Delta reflects the contribution of the repair process. Delta is derived from the repair model and can be estimated by bootstrap, multiple imputation variance, or analytic corrections.

Applications are found in survey statistics with post-imputation adjustment, epidemiological analyses using cleaned data, and simulation studies evaluating robustness to data cleaning. Variants include deterministic repair, where repaired values are fixed, and stochastic repair, where repaired values are random variables with associated uncertainty.

Repairedvary intersects with related concepts such as measurement-error models, multiple imputation variance, and bootstrap-based variance estimation. It is distinct from variance-stabilizing transformations, which pursue uniform variance across observations. Limitations include reliance on explicit repair-model assumptions and potential inflation of estimator variance if the repair model is misspecified.

There is no universally accepted standard definition for repairedvary, and references in the literature remain scattered and precautionary. See also measurement error models; multiple imputation; bootstrap methods; data cleaning.