Flerparametriske

Flerparametriske is a Norwegian term used to describe systems, models, or analyses that involve more than one parameter. The word combines fler- meaning "many" with parameter and the suffix -isk, yielding "multi-parameter" or "multi-parametric." In technical usage, it denotes problems where several parameters must be estimated or specified simultaneously, as opposed to descriptions that rely on a single parameter.

In statistics and data analysis, flerparametriske modeller include multivariate distributions, regression models with multiple coefficients, and parametric models with several shape or scale parameters. Examples are the multivariate normal distribution, where the parameter set includes a mean vector and a covariance matrix, and linear or generalized linear models with multiple regression coefficients. In engineering and the life sciences, flerparametriske approaches appear in parameterized models of dynamic systems, pharmacokinetics, and signal processing, where several parameters control system behavior.

Inference for flerparametriske modeller typically requires larger data sets or regularization due to issues such as identifiability and parameter correlation. Common estimation methods include maximum likelihood estimation, Bayesian inference with priors, and penalized likelihood or shrinkage techniques. Model checking and sensitivity analysis are important to assess identifiability and the robustness of conclusions.

The term is used mainly in Norwegian-language technical literature and contexts, but the concept corresponds to the broader English-language notions of multi-parameter or multiparametric modeling, multivariate statistics, and high-dimensional estimation.