Adaptive LASSO-type estimation for ergodic diffusion processes

TL;DR

This paper introduces an adaptive LASSO method for estimating parameters in ergodic diffusion processes, providing theoretical properties, asymptotic distribution, and demonstrating its practical effectiveness through simulations and real data analysis.

Contribution

It extends adaptive LASSO techniques to ergodic diffusion processes, establishing oracle properties and asymptotic distributions within a general statistical framework.

Findings

Proves oracle properties for the adaptive LASSO estimator.

Derives the asymptotic distribution of the estimator.

Demonstrates applicability through simulations and real data.

Abstract

The LASSO is a widely used statistical methodology for simultaneous estimation and variable selection. In the last years, many authors analyzed this technique from a theoretical and applied point of view. We introduce and study the adaptive LASSO problem for discretely observed ergodic diffusion processes. We prove oracle properties also deriving the asymptotic distribution of the LASSO estimator. Our theoretical framework is based on the random field approach and it applied to more general families of regular statistical experiments in the sense of Ibragimov-Hasminskii (1981). Furthermore, we perform a simulation and real data analysis to provide some evidence on the applicability of this method.