Department of Statistics, Faculty of Mathematical Science, Shahid Beheshti University, Tehran, Iran
10.22034/jsmta.2024.21371.1135
Abstract
This paper proposes a model to analyze longitudinal rank responses using a Bayesian approach with a random effects framework. We consider rank responses that are implicitly determined by their latent variables. Further, the usual univariate model, as well as a multivariate model, is also considered for analyzing the multiple longitudinal rank responses. We use random effect vectors to evaluate the correlation between individual responses across time. Also, a Bayesian approach that is used to yield Bayesian estimates of the model's parameters. Some simulation studies are conducted to estimate the parameters of the considered models. The model is used for a neurocognitive data set of Glioma patients who underwent surgery. The results of the data analysis are presented to illustrate the method.
Bahrami Samani, E. (2024). A model for multivariate longitudinal rank data with application to glioma patients. Journal of Statistical Modelling: Theory and Applications, 5(1), 95-116. doi: 10.22034/jsmta.2024.21371.1135
MLA
Ehsan Bahrami Samani. "A model for multivariate longitudinal rank data with application to glioma patients", Journal of Statistical Modelling: Theory and Applications, 5, 1, 2024, 95-116. doi: 10.22034/jsmta.2024.21371.1135
HARVARD
Bahrami Samani, E. (2024). 'A model for multivariate longitudinal rank data with application to glioma patients', Journal of Statistical Modelling: Theory and Applications, 5(1), pp. 95-116. doi: 10.22034/jsmta.2024.21371.1135
VANCOUVER
Bahrami Samani, E. A model for multivariate longitudinal rank data with application to glioma patients. Journal of Statistical Modelling: Theory and Applications, 2024; 5(1): 95-116. doi: 10.22034/jsmta.2024.21371.1135
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