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...
Ausführliche Beschreibung
Autor*in: |
Thanoon, Thanoon Y [verfasserIn] |
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Artikel |
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Sprache: |
Englisch |
Erschienen: |
2017 |
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Rechteinformationen: |
Nutzungsrecht: © 2017 Taylor & Francis Group, LLC 2017 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Communications in statistics / Simulation and computation - New York, NY : Dekker, 1982, 46(2017), 6, Seite 4578-22 |
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volume:46 ; year:2017 ; number:6 ; pages:4578-22 |
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DOI / URN: |
10.1080/03610918.2015.1122052 |
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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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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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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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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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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. |
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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 |
url |
http://dx.doi.org/10.1080/03610918.2015.1122052 http://www.tandfonline.com/doi/abs/10.1080/03610918.2015.1122052 https://search.proquest.com/docview/1920010776 |
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Adnan, Robiah |
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10.1080/03610918.2015.1122052 |
up_date |
2024-07-03T23:50:13.320Z |
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