Modellfits

Modellfits is a term used in statistics and data analysis to refer to the results of fitting a statistical model to data, or more generally to the adequacy of a model in describing observed data. In practice, a Modellfit encompasses the estimated parameters, the residuals, and the overall fit quality for a given model specification. The plural form Modellfits can denote multiple fitted models or fit evaluations across alternative specifications.

Assessing a Modellfit involves examining how well the model explains the data and how it generalizes to new observations. Common metrics include R-squared and adjusted R-squared for linear-type models, root mean square error (RMSE) or mean absolute error (MAE) for predictive accuracy, and deviance or log-likelihood for likelihood-based models. Information criteria such as AIC and BIC are often used to compare non-nested models. Cross-validation and bootstrapping provide estimates of predictive performance and stability of the fit.

Parameter estimation methods used to obtain a Modellfit include least squares, maximum likelihood, and Bayesian inference, depending on the model class. The fitting process also entails diagnostic checks, such as residual analysis, to detect patterns that suggest misspecification, heteroscedasticity, or outliers. Regularization techniques and model selection procedures help prevent overfitting and improve out-of-sample performance.

Applications of Modellfits span linear and nonlinear regression, generalized linear models, time-series and survival analysis, and broader machine learning pipelines. They emphasize not only describing the observed data but also achieving reliable, generalizable predictions.

See also: model validation, model selection, cross-validation, overfitting, regularization, AIC, BIC.