kurvevalg

Kurvevalg is a term used in Norwegian and Danish statistical practice to describe the process of selecting an appropriate curve to describe the relationship between variables in data analysis. It covers both choosing the functional form of the model and the method used to fit that model, with an emphasis on balancing fidelity to the data, parsimony, and interpretability.

In practice, kurvevalg involves several steps. Analysts explore the data visually and statistically, propose candidate models or curve shapes, fit the chosen models, and then evaluate their performance. Common choices include parametric forms such as linear, polynomial, exponential, or logarithmic curves, as well as nonparametric approaches like splines or LOESS. Semi-parametric methods, such as generalized additive models, combine elements of both.

Evaluation is central to kurvevalg. Criteria often include measures of fit (such as R-squared), information criteria (AIC or BIC), and predictive accuracy assessed through cross-validation or out-of-sample testing. Residual analysis and diagnostic checks help detect systematic deviations or overfitting. The final selection aims for a curve that captures the underlying pattern without modeling random noise, while remaining interpretable and suitable for extrapolation within reasonable bounds.

Applications of kurvevalg span various fields, including econometrics (modeling demand or growth curves), biology (dose-response relationships), engineering (stress–strain or performance curves), and environmental science (trend analyses). The concept is closely related to broader topics in curve fitting and model selection, but emphasizes the practical choice of a curve form tailored to the data and purpose at hand.