Bayesian analysis of multiple group nonlinear structural equation models with ordered categorical data
In this paper ordered categorical variables are used in Bayesian multiple group nonlinear structural equation mode ls and the Gibbs sampling method is applied for estimation and model comparison. Hidden continuous normal distribution (Censored normal distribution) is used to solve the problem of ord...
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| தலைமை எழà¯à®¤à¯à®¤à®¾à®³à®°à¯à®•à®³à¯: | , , |
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| வடிவமà¯: | Conference or Workshop Item |
| வெளியீடபà¯à®ªà®Ÿà¯à®Ÿà®¤à¯: |
2015
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| பகà¯à®¤à®¿à®•à®³à¯: | |
| நிகழà¯à®¨à®¿à®²à¯ˆ அணà¯à®•à®²à¯: | http://eprints.utm.my/62297/ http://eprints.utm.my/62297/ |
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| தொகà¯à®ªà¯à®ªà¯: | In this paper ordered categorical variables are used in Bayesian multiple group nonlinear structural equation mode ls and the Gibbs sampling method is applied for estimation and model comparison. Hidden continuous normal distribution (Censored normal distribution) is used to solve the problem of ordered categorical data in Bayesian multiple group SEMs and compared with the method that treats ordered categorical variables as a continuous normal distribution. Statistical inferences. which involve the estimation of parameters and their standard deviations. and residuals analyses for testing the posited model arc discussed. The proposed procedure is illustrated using real data with the results obtained from the WinBUGS program. |
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