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)1-12
Number of pages12
JournalCommunications in Statistics Part B: Simulation and Computation
DOIs
Publication statusAccepted/In press - 2016 Nov 1
Externally publishedYes

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

Estimation of principal points for a multivariate binary distribution using a log-linear model. / Yamashita, Haruka; Matsuura, Shun; Suzuki, Hideo.

In: Communications in Statistics Part B: Simulation and Computation, 01.11.2016, p. 1-12.

Research output: Contribution to journalArticle

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