Estimation of principal points for a multivariate binary distribution using a log-linear model

Haruka Yamashita, Shun Matsuura, Hideo Suzuki

Research output: Contribution to journalArticle

Abstract

This article proposes a method for estimating principal points for a multivariate binary distribution, assuming a log-linear model for the distribution. Through numerical simulation studies, the proposed parametric estimation method using a log-linear model is compared with a nonparametric estimation method.

Original languageEnglish
Pages (from-to)1136-1147
Number of pages12
JournalCommunications in Statistics: Simulation and Computation
Volume46
Issue number2
DOIs
Publication statusPublished - 2017 Feb 7

Fingerprint

Principal Points
Log-linear Models
Binary
Parametric Estimation
Nonparametric Estimation
Computer simulation
Simulation Study
Numerical Simulation

Keywords

  • Log-linear model
  • Maximum likelihood estimation
  • Multinomial distribution
  • Principal points

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation

Cite this

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title = "Estimation of principal points for a multivariate binary distribution using a log-linear model",
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