# Parametric estimation for a signal-plus-noise model from discrete time observations

## TL;DR

This paper investigates the statistical inference of signals embedded in Gaussian noise from discrete, possibly irregular, observations, establishing the consistency and efficiency of estimators for large sample sizes.

## Contribution

It provides theoretical results on the consistency and minimax efficiency of maximum likelihood and Bayesian estimators in a general signal-plus-noise model with irregular sampling.

## Key findings

- Proves consistency of estimators as observation time increases.
- Establishes minimax efficiency of estimators under various sampling schemes.
- Includes a broad class of signals, such as almost periodic and non-continuous periodic signals.

## Abstract

This paper deals with the parametric inference for integrated signals embedded in an additive Gaussian noise and observed at deterministic discrete instants which are not necessarily equidistant. The unknown parameter is multidimensional and compounded of a signal-of-interest parameter and a variance parameter of the noise. We state the consistency and the minimax efficiency of the maximum likelihood estimator and of the Bayesian estimator when the time of observation tends to $\infty$ and the delays between two consecutive observations tend to 0 or are only bounded. The class of signals in consideration contains among others, almost periodic signals and also non-continuous periodic signals. However the problem of frequency estimation is not considered here.