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:: Volume 16, Issue 2 (3-2020) ::
JSRI 2020, 16(2): 559-569 Back to browse issues page
A Note on the Identifiability of General Bayesian Gaussian Models
Amirhossein Ghatari 1, Ashkan Shabbak2 , Elham Tabrizi3
1- Amirkabir University of Technology , a.h.ghatari@aut.ac.ir
2- Statistical Research and Training Center
3- Kharazmi University
Abstract:   (1105 Views)
The main aim of this paper is to investigate the identifiability of Bayesian Gaussian regression model. The model is extensively implemented in the various Bayesian modeling concepts such as model fitting and model selection approaches. In accordance with the outcomes, the Bayesian Gaussian model is identifiable when the model's design matrix is full rank.
Keywords: Bayesian statistics, design matrix, Gaussian model, identifiability, Posterior distribution.
Full-Text [PDF 198 kb]   (382 Downloads)    
Type of Study: Research | Subject: General
Received: 2021/08/7 | Accepted: 2021/11/30 | Published: 2022/03/8
1. Aldrich, J.H. and Nelson, F.D. (1984). Linear Probability, Logit, and Probit Models. Sage, United States. [DOI:10.4135/9781412984744]
2. Bahrami Samani, E. (2014). Sensitivity Analysis for the Identifiability with Application to Latent Random Effect Model for the Mixed Data. Journal of Applied Statistics, 41, 2761-2776. [DOI:10.1080/02664763.2014.929641]
3. Christensen, R. (2011). Plane Answers to Complex Questions: The Theory of Linear Models. Springer Science & Business Media, USA.
4. Dawid, A.P. (1979). Conditional Independence in Statistical Theory. Journal of the Royal Statistical Society: Series B (Methodological), 41, 1-15. [DOI:10.1111/j.2517-6161.1979.tb01052.x]
5. De Leon, A.R., and Chough, K.C. (2013). Analysis of Mixed Data: Methods & Applications. CRC Press, United Kingdom. [DOI:10.1201/b14571]
6. Fahrmeir, L., Kneib, T., and Lang, S. (2009). Lineare Regressionsmodelle. Springer. [DOI:10.1007/978-3-642-01837-4_3]
7. Gelfand, A.E. and Sahu, S.K. (1999). Identifiability, Improper Priors, and Gibbs Sampling for Generalized Linear Models. Journal of the American Statistical Association, 94, 247-253. [DOI:10.1080/01621459.1999.10473840]
8. Ghatari, A.H., and Ganjali, M. (2020). An Analysis on Covariates Selection Problem for Gaussian Model by Maximum a Posteriori Criterion Using Frequentist and Bayesian Approaches. Journal of Advanced Mathematical Modeling, 10, 245-266.
9. Kass, R.E., and Raftery, A.E. (1995). Bayes Factors. Journal of the American Statistical Association, 90, 773-795. [DOI:10.1080/01621459.1995.10476572]
10. Lindley, D.V. (1972). BayesianStatistics:AReview. SIAM, UnitedStates. [DOI:10.1137/1.9781611970654]
11. Martin, E.S., and Quintana, F. (2002). Consistency and Identifiability Revisited, Brazilian Journal of Probability and Statistics, 16, 99-106.
12. Miao, W., Ding, P., and Geng, Z. (2016). Identifiability of Normal and Normal Mixture Models with Nonignorable Missing Data. Journal of the American Statistical Association, 111, 1673-1683. [DOI:10.1080/01621459.2015.1105808]
13. Piironen, J., and Vehtari, A. (2017). Comparison of Bayesian Predictive Methods for Model Selection. Statistics and Computing, 27, 711-735. [DOI:10.1007/s11222-016-9649-y]
14. Tabrizi, E., Bahrami Samani, E., and Ganjali, M. (2020a). Identifiability of Parameters in Longitudinal Correlated Poisson and Inflated Beta Regression Model with Non-ignorable Missing Mechanism. Statistics, 54, 524-543. [DOI:10.1080/02331888.2020.1748883]
15. Tabrizi, E., Bahrami Samani, E., and Ganjali, M. (2020b). A Note on the Identifiability of Latent Variable Models for Mixed Longitudinal data. Statistics & Probability Letters, 167, 1-5. [DOI:10.1016/j.spl.2020.108882]
16. Vehtari, A., and Ojanen, J. (2012). A Survey of Bayesian Predictive Methods for Model Assessment, Selection and Comparison. Statistics Surveys, 6, 142-228. [DOI:10.1214/12-SS102]
17. Wang, W. (2013). Identifiability of Linear Mixed Effects Models. Institute of Mathematical Statistics and Bernoulli Society, 7, 244-263. [DOI:10.1214/13-EJS770]
18. Yu, Q., and Dong, J. (2020). Identifiability Conditions for the Linear Regression Model Under Right Censoring, Communications in Statistics-Theory and Methods, 1-19. [DOI:10.1080/03610926.2020.1743315]
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Ghatari A, Shabbak A, Tabrizi E. A Note on the Identifiability of General Bayesian Gaussian Models. JSRI 2020; 16 (2) :559-569
URL: http://jsri.srtc.ac.ir/article-1-395-en.html

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Volume 16, Issue 2 (3-2020) Back to browse issues page
مجله‌ی پژوهش‌های آماری ایران Journal of Statistical Research of Iran JSRI
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