# On asymptotically efficient maximum likelihood estimation of linear functionals in Laplace measurement error models

## TL;DR

This paper investigates the conditions under which maximum likelihood estimation can efficiently estimate linear functionals in Laplace measurement error models, revealing fundamental limitations and the absence of regular estimators at parametric rates.

## Contribution

It characterizes when the MLE achieves asymptotic efficiency for linear functionals in Laplace deconvolution and demonstrates the fundamental limitations in achieving parametric rates.

## Key findings

- MLE can be asymptotically efficient for some functionals
- No regular estimator can achieve parametric rate in general
- Estimation often requires slower rates with non-Gaussian limits

## Abstract

Maximum likelihood estimation of linear functionals in the inverse problem of deconvolution is considered. Given observations of a random sample from a distribution $P_0\equiv P_{F_0}$ indexed by a (potentially infinite-dimensional) parameter $F_0$, which is the distribution of the latent variable in a standard additive Laplace measurement error model, one wants to estimate a linear functional of $F_0$. Asymptotically efficient maximum likelihood estimation (MLE) of integral linear functionals of the mixing distribution $F_0$ in a convolution model with the Laplace kernel density is investigated. Situations are distinguished in which the functional of interest can be consistently estimated at $n^{-1/2}$-rate by the plug-in MLE, which is asymptotically normal and efficient, in the sense of achieving the variance lower bound, from those in which no integral linear functional can be estimated at parametric rate, which precludes any possibility for asymptotic efficiency. The $\sqrt{n}$-convergence of the MLE, valid in the case of a degenerate mixing distribution at a single location point, fails in general, as does asymptotic normality. It is shown that there exists no regular estimator sequence for integral linear functionals of the mixing distribution that, when recentered about the estimand and $\sqrt{n}$-rescaled, is asymptotically efficient, \emph{viz}., has Gaussian limit distribution with minimum variance. One can thus only expect estimation with some slower rate and, often, with a non-Gaussian limit distribution.