Laplacian Pyramids Multi-scale Regression with Applications

Regression methods provide a simple mechanism for evaluating empirical functions over scattered data points, allowing to forecast future values. In particular, kernel-based regressions, such as the Nadaraya-Watson estimator are suitable for cases in which the relationship between the data points and the function is not linear. Successive applications of the Nadarya-Watson estimator, also known as Laplacian Pyramids model or iterative bias reduction, yield a multiscale model that generates a smoothed version of a function by using Gaussian kernels to smooth the residuals. 

In this talk, I will review some recent extensions of this regression for spatio-temporal forecasting and bio-medical applications.