Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data

In this article, dichotomous variables are used to compare between linear and nonlinear Bayesian structural equation models. Gibbs sampling method is applied for estimation and model comparison. Statistical inferences that involve estimation of parameters and their standard deviations and residuals...
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Autor*in:	Thanoon, Thanoon Y [verfasserIn] Adnan, Robiah

Format:	Artikel
Sprache:	Englisch

Erschienen:	2017

Rechteinformationen:	Nutzungsrecht: © 2017 Taylor & Francis Group, LLC 2017

Schlagwörter:	Primary 62F15 Structural equation models Secondary 62M05 Latent variables Bayesian analysis Dichotomous data Gibbs sampling Parameter estimation Normal distribution Mathematical models Computer simulation

Übergeordnetes Werk:	Enthalten in: Communications in statistics / Simulation and computation - New York, NY : Dekker, 1982, 46(2017), 6, Seite 4578-22
Übergeordnetes Werk:	volume:46 ; year:2017 ; number:6 ; pages:4578-22

Links:	Volltext Link aufrufen Link aufrufen

DOI / URN:	10.1080/03610918.2015.1122052

Katalog-ID:	OLC1996107593

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allfields	10.1080/03610918.2015.1122052 doi PQ20171228 (DE-627)OLC1996107593 (DE-599)GBVOLC1996107593 (PRQ)c2197-a329fefd0de5605ac5c66096bc061669c716c84bed26106eb67e5e8de1823450 (KEY)0108850520170000046000604578modelcomparisonoflinearandnonlinearbayesianstructu DE-627 ger DE-627 rakwb eng 510 DE-600 31.73 bkl Thanoon, Thanoon Y verfasserin aut Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this article, dichotomous variables are used to compare between linear and nonlinear Bayesian structural equation models. Gibbs sampling method is applied for estimation and model comparison. Statistical inferences that involve estimation of parameters and their standard deviations and residuals analysis for testing the selected model are discussed. Hidden continuous normal distribution (censored normal distribution) is used to solve the problem of dichotomous variables. The proposed procedure is illustrated by a simulation data obtained from R program. Analyses are done by using R2WinBUGS package in R-program. Nutzungsrecht: © 2017 Taylor & Francis Group, LLC 2017 Primary 62F15 Structural equation models Secondary 62M05 Latent variables Bayesian analysis Dichotomous data Gibbs sampling Parameter estimation Normal distribution Mathematical models Computer simulation Adnan, Robiah oth Enthalten in Communications in statistics / Simulation and computation New York, NY : Dekker, 1982 46(2017), 6, Seite 4578-22 (DE-627)129862258 (DE-600)283664-6 (DE-576)015173682 0361-0918 nnns volume:46 year:2017 number:6 pages:4578-22 http://dx.doi.org/10.1080/03610918.2015.1122052 Volltext http://www.tandfonline.com/doi/abs/10.1080/03610918.2015.1122052 https://search.proquest.com/docview/1920010776 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 31.73 AVZ AR 46 2017 6 4578-22
spelling	10.1080/03610918.2015.1122052 doi PQ20171228 (DE-627)OLC1996107593 (DE-599)GBVOLC1996107593 (PRQ)c2197-a329fefd0de5605ac5c66096bc061669c716c84bed26106eb67e5e8de1823450 (KEY)0108850520170000046000604578modelcomparisonoflinearandnonlinearbayesianstructu DE-627 ger DE-627 rakwb eng 510 DE-600 31.73 bkl Thanoon, Thanoon Y verfasserin aut Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this article, dichotomous variables are used to compare between linear and nonlinear Bayesian structural equation models. Gibbs sampling method is applied for estimation and model comparison. Statistical inferences that involve estimation of parameters and their standard deviations and residuals analysis for testing the selected model are discussed. Hidden continuous normal distribution (censored normal distribution) is used to solve the problem of dichotomous variables. The proposed procedure is illustrated by a simulation data obtained from R program. Analyses are done by using R2WinBUGS package in R-program. Nutzungsrecht: © 2017 Taylor & Francis Group, LLC 2017 Primary 62F15 Structural equation models Secondary 62M05 Latent variables Bayesian analysis Dichotomous data Gibbs sampling Parameter estimation Normal distribution Mathematical models Computer simulation Adnan, Robiah oth Enthalten in Communications in statistics / Simulation and computation New York, NY : Dekker, 1982 46(2017), 6, Seite 4578-22 (DE-627)129862258 (DE-600)283664-6 (DE-576)015173682 0361-0918 nnns volume:46 year:2017 number:6 pages:4578-22 http://dx.doi.org/10.1080/03610918.2015.1122052 Volltext http://www.tandfonline.com/doi/abs/10.1080/03610918.2015.1122052 https://search.proquest.com/docview/1920010776 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 31.73 AVZ AR 46 2017 6 4578-22
allfields_unstemmed	10.1080/03610918.2015.1122052 doi PQ20171228 (DE-627)OLC1996107593 (DE-599)GBVOLC1996107593 (PRQ)c2197-a329fefd0de5605ac5c66096bc061669c716c84bed26106eb67e5e8de1823450 (KEY)0108850520170000046000604578modelcomparisonoflinearandnonlinearbayesianstructu DE-627 ger DE-627 rakwb eng 510 DE-600 31.73 bkl Thanoon, Thanoon Y verfasserin aut Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this article, dichotomous variables are used to compare between linear and nonlinear Bayesian structural equation models. Gibbs sampling method is applied for estimation and model comparison. Statistical inferences that involve estimation of parameters and their standard deviations and residuals analysis for testing the selected model are discussed. Hidden continuous normal distribution (censored normal distribution) is used to solve the problem of dichotomous variables. The proposed procedure is illustrated by a simulation data obtained from R program. Analyses are done by using R2WinBUGS package in R-program. Nutzungsrecht: © 2017 Taylor & Francis Group, LLC 2017 Primary 62F15 Structural equation models Secondary 62M05 Latent variables Bayesian analysis Dichotomous data Gibbs sampling Parameter estimation Normal distribution Mathematical models Computer simulation Adnan, Robiah oth Enthalten in Communications in statistics / Simulation and computation New York, NY : Dekker, 1982 46(2017), 6, Seite 4578-22 (DE-627)129862258 (DE-600)283664-6 (DE-576)015173682 0361-0918 nnns volume:46 year:2017 number:6 pages:4578-22 http://dx.doi.org/10.1080/03610918.2015.1122052 Volltext http://www.tandfonline.com/doi/abs/10.1080/03610918.2015.1122052 https://search.proquest.com/docview/1920010776 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 31.73 AVZ AR 46 2017 6 4578-22
allfieldsGer	10.1080/03610918.2015.1122052 doi PQ20171228 (DE-627)OLC1996107593 (DE-599)GBVOLC1996107593 (PRQ)c2197-a329fefd0de5605ac5c66096bc061669c716c84bed26106eb67e5e8de1823450 (KEY)0108850520170000046000604578modelcomparisonoflinearandnonlinearbayesianstructu DE-627 ger DE-627 rakwb eng 510 DE-600 31.73 bkl Thanoon, Thanoon Y verfasserin aut Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this article, dichotomous variables are used to compare between linear and nonlinear Bayesian structural equation models. Gibbs sampling method is applied for estimation and model comparison. Statistical inferences that involve estimation of parameters and their standard deviations and residuals analysis for testing the selected model are discussed. Hidden continuous normal distribution (censored normal distribution) is used to solve the problem of dichotomous variables. The proposed procedure is illustrated by a simulation data obtained from R program. Analyses are done by using R2WinBUGS package in R-program. Nutzungsrecht: © 2017 Taylor & Francis Group, LLC 2017 Primary 62F15 Structural equation models Secondary 62M05 Latent variables Bayesian analysis Dichotomous data Gibbs sampling Parameter estimation Normal distribution Mathematical models Computer simulation Adnan, Robiah oth Enthalten in Communications in statistics / Simulation and computation New York, NY : Dekker, 1982 46(2017), 6, Seite 4578-22 (DE-627)129862258 (DE-600)283664-6 (DE-576)015173682 0361-0918 nnns volume:46 year:2017 number:6 pages:4578-22 http://dx.doi.org/10.1080/03610918.2015.1122052 Volltext http://www.tandfonline.com/doi/abs/10.1080/03610918.2015.1122052 https://search.proquest.com/docview/1920010776 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 31.73 AVZ AR 46 2017 6 4578-22
allfieldsSound	10.1080/03610918.2015.1122052 doi PQ20171228 (DE-627)OLC1996107593 (DE-599)GBVOLC1996107593 (PRQ)c2197-a329fefd0de5605ac5c66096bc061669c716c84bed26106eb67e5e8de1823450 (KEY)0108850520170000046000604578modelcomparisonoflinearandnonlinearbayesianstructu DE-627 ger DE-627 rakwb eng 510 DE-600 31.73 bkl Thanoon, Thanoon Y verfasserin aut Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this article, dichotomous variables are used to compare between linear and nonlinear Bayesian structural equation models. Gibbs sampling method is applied for estimation and model comparison. Statistical inferences that involve estimation of parameters and their standard deviations and residuals analysis for testing the selected model are discussed. Hidden continuous normal distribution (censored normal distribution) is used to solve the problem of dichotomous variables. The proposed procedure is illustrated by a simulation data obtained from R program. Analyses are done by using R2WinBUGS package in R-program. Nutzungsrecht: © 2017 Taylor & Francis Group, LLC 2017 Primary 62F15 Structural equation models Secondary 62M05 Latent variables Bayesian analysis Dichotomous data Gibbs sampling Parameter estimation Normal distribution Mathematical models Computer simulation Adnan, Robiah oth Enthalten in Communications in statistics / Simulation and computation New York, NY : Dekker, 1982 46(2017), 6, Seite 4578-22 (DE-627)129862258 (DE-600)283664-6 (DE-576)015173682 0361-0918 nnns volume:46 year:2017 number:6 pages:4578-22 http://dx.doi.org/10.1080/03610918.2015.1122052 Volltext http://www.tandfonline.com/doi/abs/10.1080/03610918.2015.1122052 https://search.proquest.com/docview/1920010776 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 31.73 AVZ AR 46 2017 6 4578-22
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spellingShingle	Thanoon, Thanoon Y ddc 510 bkl 31.73 misc Primary 62F15 misc Structural equation models misc Secondary 62M05 misc Latent variables misc Bayesian analysis misc Dichotomous data misc Gibbs sampling misc Parameter estimation misc Normal distribution misc Mathematical models misc Computer simulation Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data
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topic_title	510 DE-600 31.73 bkl Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data Primary 62F15 Structural equation models Secondary 62M05 Latent variables Bayesian analysis Dichotomous data Gibbs sampling Parameter estimation Normal distribution Mathematical models Computer simulation
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title	Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data
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title_auth	Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data
abstract	In this article, dichotomous variables are used to compare between linear and nonlinear Bayesian structural equation models. Gibbs sampling method is applied for estimation and model comparison. Statistical inferences that involve estimation of parameters and their standard deviations and residuals analysis for testing the selected model are discussed. Hidden continuous normal distribution (censored normal distribution) is used to solve the problem of dichotomous variables. The proposed procedure is illustrated by a simulation data obtained from R program. Analyses are done by using R2WinBUGS package in R-program.
abstractGer	In this article, dichotomous variables are used to compare between linear and nonlinear Bayesian structural equation models. Gibbs sampling method is applied for estimation and model comparison. Statistical inferences that involve estimation of parameters and their standard deviations and residuals analysis for testing the selected model are discussed. Hidden continuous normal distribution (censored normal distribution) is used to solve the problem of dichotomous variables. The proposed procedure is illustrated by a simulation data obtained from R program. Analyses are done by using R2WinBUGS package in R-program.
abstract_unstemmed	In this article, dichotomous variables are used to compare between linear and nonlinear Bayesian structural equation models. Gibbs sampling method is applied for estimation and model comparison. Statistical inferences that involve estimation of parameters and their standard deviations and residuals analysis for testing the selected model are discussed. Hidden continuous normal distribution (censored normal distribution) is used to solve the problem of dichotomous variables. The proposed procedure is illustrated by a simulation data obtained from R program. Analyses are done by using R2WinBUGS package in R-program.
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title_short	Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data
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ppnlink	129862258
mediatype_str_mv	n
isOA_txt	false
hochschulschrift_bool	false
author2_role	oth
doi_str	10.1080/03610918.2015.1122052
up_date	2024-07-03T23:50:13.320Z
_version_	1803603791914729472
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Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data

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