Sieve Approximation

A technique in statistical learning theory for handling high-dimensional or nonparametric estimation. It approximates an infinite-dimensional target function space with a sequence of finite-dimensional function spaces of increasing size, thereby balancing bias against variance and enabling tractable estimation and consistency analysis of nonparametric estimators.