On polychoric and polyserial partial correlation coefficients: a Bayesian approach


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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/53020

Title:	On polychoric and polyserial partial correlation coefficients: a Bayesian approach
Authors:	Hasegawa, Hikaru Browse this author →KAKEN DB
Keywords:	Gibbs sampler
	Markov chain Monte Carlo (MCMC)
	Metropolis-Hastings (M-H) algorithm
	Well-being
Issue Date:	Sep-2013
Publisher:	Springer
Journal Title:	METRON
Volume:	71
Issue:	2
Start Page:	139
End Page:	156
Publisher DOI:	10.1007/s40300-013-0012-1
Abstract:	This article provides the estimation method for multivariate polychoric and polyserial correlation coefficients by using the simulation-based Bayesian method. It also shows that the partial version of the polychoric and polyserial correlation coefficients can be estimated using the corresponding estimates of the simple version. A simulation study illustrates the proposed method. Further, an application of the method to subjective well-being data is provided.
Rights:	The final publication is available at www.springerlink.com
Type:	article (author version)
URI:	http://hdl.handle.net/2115/53020
Appears in Collections:	経済学院・経済学研究院 (Graduate School of Economics and Business / Faculty of Economics and Business) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 長谷川光

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