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• W2157965523 startingPage "2337" @default.
• W2157965523 abstract "Transformation latent variable models are proposed in this study to analyze multivariate censored data. The proposed models generalize conventional linear transformation models to semiparametric transformation models that accommodate latent variables. The characteristics of the latent variables were assessed based on several correlated observed indicators through measurement equations. A Bayesian approach was developed with Bayesian P-splines technique and the Markov chain Monte Carlo algorithm to estimate the unknown parameters and transformation functions. Simulation shows that the performance of the proposed methodology is satisfactory. The proposed method was applied to analyze a cardiovascular disease data set." @default.
• W2157965523 created "2016-06-24" @default.
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• W2157965523 date "2016-07-11" @default.
• W2157965523 modified "2023-09-26" @default.
• W2157965523 title "Bayesian analysis of transformation latent variable models with multivariate censored data" @default.
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• W2157965523 doi "https://doi.org/10.1177/0962280214522786" @default.
• W2157965523 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/24535555" @default.
• W2157965523 hasPublicationYear "2016" @default.
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