Abstract

The Fisher information matrix (FIM) is a foundational concept in statistical signal processing. The FIM depends on the probability distribution, assumed to belong to a smooth parametric family. Traditional approaches to estimating the FIM require estimating the probability distribution function (PDF), or its parameters, along with its gradient or Hessian. However, in many practical situations the PDF of the data is not known but the statistician has access to an observation sample for any parameter value. Here we propose a method of estimating the FIM directly from sampled data that does not require knowledge of the underlying PDF. The method is based on non-parametric estimation of an f-divergence over a local neighborhood of the parameter space and a relation between curvature of the f-divergence and the FIM. Thus we obtain an empirical estimator of the FIM that does not require density estimation and is asymptotically consistent. We empirically evaluate the validity of our approach using two experiments.

Original languageEnglish (US)
Article number6975144
Pages (from-to)988-992
Number of pages5
JournalIEEE Signal Processing Letters
Volume22
Issue number7
DOIs
StatePublished - Jul 1 2015

Keywords

  • Cochlear implant modeling
  • Cramer-Rao lower bound
  • empirical Fisher information
  • f-divergence
  • graph signal processing

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Empirical non-parametric estimation of the Fisher information'. Together they form a unique fingerprint.

  • Cite this