The Symmetric Location Problem: a Song of Efficiency and Robustness
Abstract
The aim of this Lecture Note is to introduce the Signal Processing (SP) community to a powerful yet still under-utilised tool: the semiparametric statistics. In short, the semiparametric framework allows us to estimate or perform hypothesis testing on a finite-dimensional parameter θ ∈ Θ⊆ ℝp in the presence of an \textit{infinite-dimensional nuisance parameter} g ∈ 𝒮 (i.e. a function), such as the density of the noise. Clearly, this framework is general enough to include almost every SP application. Remarkably, as the title suggests drawing on George R. R. Martin's famous book series, the greatest advantage of semiparametric statistics over parametric and non-parametric ones lies in the fact that it is able to reconcile two seemingly dichotomous concepts: statistical efficiency and distributional robustness. To explain exactly what this means, in this Lecture Note we will focus our attention on the famous and fundamental symmetric location problem.
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BibTeX
@misc{fortunati2026symmetric,
title = {{The Symmetric Location Problem: a Song of Efficiency and Robustness}},
author = {Stefano Fortunati},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2605.25870},
eprint = {2605.25870},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.25870},
}
RIS
TY - UNPB
TI - The Symmetric Location Problem: a Song of Efficiency and Robustness
AU - Stefano Fortunati
PY - 2026
DA - 2026/09/29
PB - arXiv
DO - 10.48550/arxiv.2605.25870
UR - https://arxiv.org/abs/2605.25870
AB - The aim of this Lecture Note is to introduce the Signal Processing (SP) community to a powerful yet still under-utilised tool: the semiparametric statistics. In short, the semiparametric framework allows us to estimate or perform hypothesis testing on a finite-dimensional parameter θ ∈ Θ⊆ ℝ^p in the presence of an \textit{infinite-dimensional nuisance parameter} g ∈ 𝒮 (i.e. a function), such as the density of the noise. Clearly, this framework is general enough to include almost every SP application. Remarkably, as the title suggests drawing on George R. R. Martin's famous book series, the greatest advantage of semiparametric statistics over parametric and non-parametric ones lies in the fact that it is able to reconcile two seemingly dichotomous concepts: statistical efficiency and distributional robustness. To explain exactly what this means, in this Lecture Note we will focus our attention on the famous and fundamental symmetric location problem.
ER -