Nonparametric regression for functional data provides a flexible statistical framework for modelling relationships between a scalar response and predictors that are inherently functional in nature.

Kernel estimators of a regression function are investigated. The bandwidths are locally chosen by a data-driven method based on the minimization of a local cross-validation criterion. This method is ...

Nonparametric estimation and U-statistics have emerged as vital tools in modern statistical analysis, offering robust alternatives to traditional parametric methods. Nonparametric techniques bypass ...

Minimax L₂ risks for high-dimensional nonparametric regression are derived under two sparsity assumptions: (1) the true regression surface is a sparse function that depends only on d = O(log n) ...

Bayesian field theory denotes a nonparametric Bayesian approach for learning functions from observational data. Based on the principles of Bayesian statistics, a particular Bayesian field theory is ...

SAS/INSIGHT software provides nonparametric curve-fitting estimates from smoothing spline, kernel, loess, and fixed bandwidth local polynomial estimators that are alternatives to fitting polynomials.

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of Nadaraya-Watson kernel regression using the C# language. NW kernel regression is simple to implement and is ...