# Classical and bayesian componentwise predictors for non-compact correlated ARH(1) processes

## TL;DR

This paper investigates classical and Bayesian componentwise predictors for a special class of Gaussian ARH(1) processes with non-Hilbert-Schmidt autocorrelation operators, providing theoretical analysis and simulation validation.

## Contribution

It introduces and analyzes diagonal classical and Bayesian estimators for non-compact ARH(1) processes, proving their asymptotic efficiency and equivalence.

## Key findings

- Both estimators are asymptotically efficient.
- Classical and Bayesian predictors are asymptotically equivalent.
- Simulation confirms theoretical results.

## Abstract

A special class of standard Gaussian Autoregressive Hilbertian processes of order one (Gaussian ARH(1) processes), with bounded linear autocorrelation operator, which does not satisfy the usual Hilbert-Schmidt assumption, is considered. To compensate the slow decay of the diagonal coefficients of the autocorrelation operator, a faster decay velocity of the eigenvalues of the trace autocovariance operator of the innovation process is assumed. As usual, the eigenvectors of the autocovariance operator of the ARH(1) process are considered for projection, since, here, they are assumed to be known. Diagonal componentwise classical and bayesian estimation of the autocorrelation operator is studied for prediction. The asymptotic efficiency and equivalence of both estimators is proved, as well as of their associated componentwise ARH(1) plugin predictors. A simulation study is undertaken to illustrate the theoretical results derived.