Model Selection via Misspecified Cramér-Rao Bound Minimization.

Nadav E. Rosenthal, Joseph Tabrikian

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In many applications of estimation theory, the true data model is unknown, and a set of parameterized models are used to approximate it. This problem is encountered in learning systems, where the assumed model parameters are estimated using training data. One of the challenges in these problems is choosing the architecture used for the approximated model. Complex and high-order models with limited training data size may lead to overfitting, while simple and low-order models may lead to model misspecification. In this paper, we propose to use the misspecified Cramér-Rao bound (MCRB) as a criterion for model selection. The MCRB takes into account modeling errors due to both overfitting and model misspecification. The performance of the proposed approach is evaluated via simulations for model order selection in a linear regression problem. The proposed method outperforms the minimum description length and the Akaike information criterion.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5762-5766
Number of pages5
ISBN (Electronic)9781665405409
DOIs
StatePublished - 2022
Event47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore
Duration: 23 May 202227 May 2022

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2022-May
ISSN (Print)1520-6149

Conference

Conference47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Country/TerritorySingapore
CityVirtual, Online
Period23/05/2227/05/22

Keywords

  • AIC
  • MDL
  • misspecified Cramér-Rao bound (MCRB)
  • model misspecification
  • Model order selection
  • overfitting

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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