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

Haruka Yamashita; Shun Matsuura; Hideo Suzuki

doi:10.1080/03610918.2014.992541

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

Haruka Yamashita, Shun Matsuura, Hideo Suzuki

Department of Industrial and Systems Engineering

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

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 language	English
Pages (from-to)	1136-1147
Number of pages	12
Journal	Communications in Statistics: Simulation and Computation
Volume	46
Issue number	2
DOIs	https://doi.org/10.1080/03610918.2014.992541
Publication status	Published - 2017 Feb 7

Keywords

Log-linear model
Maximum likelihood estimation
Multinomial distribution
Principal points

ASJC Scopus subject areas

Statistics and Probability
Modelling and Simulation

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10.1080/03610918.2014.992541

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

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.",

keywords = "Log-linear model, Maximum likelihood estimation, Multinomial distribution, Principal points",

author = "Haruka Yamashita and Shun Matsuura and Hideo Suzuki",

note = "Funding Information: The authors would like to thank two anonymous reviewers for their constructive comments. Yamashita{\textquoteright}s portion of this work was supported by JSPS KAKENHI Grant Number 24·6963. Matsuura{\textquoteright}s portion of this work was supported by JSPS KAKENHI Grant Number 23700341. Publisher Copyright: {\textcopyright} 2017 Taylor & Francis Group, LLC.",

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AU - Matsuura, Shun

AU - Suzuki, Hideo

N1 - Funding Information: The authors would like to thank two anonymous reviewers for their constructive comments. Yamashita’s portion of this work was supported by JSPS KAKENHI Grant Number 24·6963. Matsuura’s portion of this work was supported by JSPS KAKENHI Grant Number 23700341. Publisher Copyright: © 2017 Taylor & Francis Group, LLC.

PY - 2017/2/7

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N2 - 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.

AB - 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.

KW - Log-linear model

KW - Maximum likelihood estimation

KW - Multinomial distribution

KW - Principal points

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

Abstract

Keywords

ASJC Scopus subject areas

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