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

^*Corresponding author for this work

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)	1-12
Number of pages	12
Journal	Communications in Statistics Part B: Simulation and Computation
DOIs	https://doi.org/10.1080/03610918.2014.992541
Publication status	Accepted/In press - 2016 Nov 1
Externally published	Yes

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

Cite this

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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",

year = "2016",

month = nov,

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doi = "10.1080/03610918.2014.992541",

language = "English",

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journal = "Communications in Statistics Part B: Simulation and Computation",

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T1 - Estimation of principal points for a multivariate binary distribution using a log-linear model

AU - Yamashita, Haruka

AU - Matsuura, Shun

AU - Suzuki, Hideo

PY - 2016/11/1

Y1 - 2016/11/1

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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M3 - Article

AN - SCOPUS:84994182286

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JO - Communications in Statistics Part B: Simulation and Computation

JF - Communications in Statistics Part B: Simulation and Computation

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

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