Residualer

Residualer is a diagnostic framework designed to analyze the structure of residuals produced by predictive models. It aims to identify misspecification, nonlinearity, and heteroscedasticity by transforming and analyzing residuals in a standardized way across different modeling contexts.

Core components of Residualer include residual estimation, residual transformation, and diagnostic visualization. The method emphasizes robust, scale-invariant residuals by using median-based measures and variance-stabilizing transforms, producing residual scores that can be compared across datasets and subgroups. Outputs commonly include standardized residuals, residual plots against fitted values and covariates, and summary statistics of residual dispersion.

Applications of Residualer span statistics, econometrics, machine learning diagnostics, and engineering. It is used to assess model adequacy, guide model refinement, and compare alternative models. The framework supports both cross-sectional and time-series data and can be integrated with common modeling pipelines.

Limitations should be considered when applying Residualer. Interpretation depends on the underlying model, and extreme outliers may distort results without appropriate handling. The approach complements, rather than replaces, existing diagnostics such as Q-Q plots, influence measures, and out-of-sample testing. Practitioners typically combine Residualer outputs with traditional indicators to form a comprehensive view of model performance.

See also: residuals, regression diagnostics, heteroscedasticity, Box-Cox transformation, Cook’s distance.