Stat.Nonparametric

Nonparametric methods: approximation theory, weighted least squares, local-polynomial coercivity and derivative bounds, series/sieve estimators, higher-order influence functions, and moment-problem tools.

Approximation 14 core · 17 supporting · 4 submodules Deterministic approximation-theory primitives for nonparametric bias analysis, including Hölder–Taylor remainder bounds and kernel smoothing bias estimates. Least­Squares 5 core · 6 supporting · 3 submodules Design-agnostic weighted least-squares primitives for nonparametric estimators: normal equations, projection optimality, smoother bias, and spherical-error variance. HOIF 7 core · 8 supporting · 4 submodules Higher-order influence-function machinery for localized nonparametric functionals: product remainders, degenerate U-statistic variance, projected-kernel trace identities, and projection-risk assembly. Local­Poly 25 core · 24 supporting · 6 submodules Degree-p local-polynomial regression substrate: equivalent-kernel weights, design positive definiteness, bias, variance, leverage rates, and pointwise risk bounds. Local­Polynomial 7 to review 7 core · 8 supporting · 2 submodules General local-polynomial analysis: coordinate partial derivatives, operator-norm bounds, and coercivity of weighted radial polynomial energies. Moment­Problems 68 core · 74 supporting · 8 submodules Moment-problem substrate for nonparametric statistics: raw-moment algebra, L² projection residuals, constrained score programs, and sharp bounded-outcome residual envelopes. Series­Sieve 9 core · 7 supporting · 3 submodules This barrel collects reusable series/sieve approximation and least-squares prediction tools for nonparametric regression and projection arguments.