estimationdepends

Estimationdepends is a term used in statistics and data analysis to describe the phenomenon that an estimate can depend on aspects of the estimation process beyond the observed data. The term captures situations where the numerical result changes when different estimation methods, data preprocessing choices, or prior assumptions are applied, even if the underlying data are the same.

Formal notion: If an estimator is denoted by hat theta_M,P = E(D; M, P), where D is the data, M represents the model or estimator class, and P denotes optional priors or tuning parameters, then estimationdepends describes the sensitivity of hat theta to M and P. In such cases, the estimator is not purely determined by D but also by the chosen framework. Examples include Bayesian vs frequentist estimates of the same quantity, or using different data cleaning pipelines that alter the effective data D.

Implications: Recognition of estimationdependence is important for interpretation, reproducibility, and decision making. Analysts perform sensitivity analyses, report multiple estimation schemes, or adopt robust or model-averaged approaches to mitigate excessive dependence on arbitrary choices.

See also: Estimator, sensitivity analysis, robustness, model uncertainty, prior distribution, reproducibility.