Stochastic dynamic models and Chebyshev splines.

Abstract

In this article, we establish a connection between a stochastic dynamic model (SDM) driven by a linear stochastic differential equation (SDE) and a Chebyshev spline, which enables researchers to borrow strength across fields both theoretically and numerically. We construct a differential operator for the penalty function and develop a reproducing kernel Hilbert space (RKHS) induced by the SDM and the Chebyshev spline. The general form of the linear SDE allows us to extend the well-known connection between an integrated Brownian motion and a polynomial spline to a connection between more complex diffusion processes and Chebyshev splines. One interesting special case is connection between an integrated Ornstein-Uhlenbeck process and an exponential spline. We use two real data sets to illustrate the integrated Ornstein-Uhlenbeck process model and exponential spline model and show their estimates are almost identical.