Minimalpar
Minimalpar is a principle and set of methods used in statistics and data analysis to obtain models with the smallest possible number of free parameters while maintaining acceptable predictive accuracy or explanatory power. The term highlights parsimony: simpler models are preferred when they perform comparably to more complex ones.
Formally, minimalpar can be described as solving an optimization problem that seeks to minimize the sparsity
Applications span various domains, including statistics, machine learning, signal processing, econometrics, and control theory. It is
History and terminology: The broader idea of favoring simple parameterizations has a long-standing presence in statistical
See also: parsimony, sparse modeling, regularization, minimum description length, information criteria such as AIC and BIC,