"On Estimation of Function-on-function Regression Kernels with Brownian" by Paramahansa Pramanik
 

On Estimation of Function-on-function Regression Kernels with Brownian Berkson Errors.

Document Type

Lecture

Publication Date

4-11-2024

Abstract

In this paper, we introduce a new methodology to determine an optimal kernel of function-on-function regression in the presence of a stochastic differential equation with Berkson error. We assume that the response variable, unobserved true predictor, the best available observed measure of the true predictor, and the regression kernels are functions of space and time, and the regressor dynamics follow a stochastic differential equation. First, we construct an objective function as a time-dependent Mean Square Error (MSE) and then minimize it with respect to regression coefficients subject to stochastic Berkson error dynamics. A Feynman-type path integral control approach is used to determine a Wick-rotated Schrodinger-type equation that has the complete information of the system. Using first-order conditions for these kernels give us a closed-form solution.

Relational Format

presentation

This document is currently not available for download.

Share

COinS