Statistical Estimation of Quantization for Probability Distributions: Best Equivariant Estimator of Principal Points

Shun Matsuura, Hiroshi Kurata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Quantization gives a discrete approximation (with a finite set of points called quantizer) for a probability distribution. When the approximation is optimal with respect to a loss function, it is called optimal quantization (and its set of points is called an optimal quantizer), which has been studied and applied in various areas. Especially in statistics, an optimal quantizer under a quadratic loss function (optimal quantizer of order 2) has been widely investigated and is often called a set of principal points (or simply, principal points) for a probability distribution. In practice, however, the values of the parameters of the probability distribution are sometimes unknown, and hence we have to estimate principal points based on a random sample. A common method for estimating principal points is using principal points of the empirical distribution obtained by a random sample, which can be viewed as a nonparametric estimator of principal points. Several papers discussed statistical parametric estimation of principal points based on maximum likelihood estimators of the parameters. In this paper, a class of equivariant estimators, which includes previous parametric estimators of principal points is considered, and the best equivariant estimator of principal points is derived. It turns out that, under some condition, the best equivariant estimator coincides with a previously obtained parametric estimator. However, it is also shown that, for some probability distributions not satisfying the condition, the best equivariant estimator may not be equivalent to previous estimators.

Original languageEnglish
Title of host publicationMachine Learning, Optimization, and Data Science - 7th International Conference, LOD 2021, Revised Selected Papers
EditorsGiuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Giorgio Jansen, Panos M. Pardalos, Giovanni Giuffrida, Renato Umeton
PublisherSpringer Science and Business Media Deutschland GmbH
Pages430-441
Number of pages12
ISBN (Print)9783030954666
DOIs
Publication statusPublished - 2022
Event7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021 - Virtual, Online
Duration: 2021 Oct 42021 Oct 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13163 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021
CityVirtual, Online
Period21/10/421/10/8

Keywords

  • Equivariant estimator
  • Principal points
  • Quantization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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