Bayesian latent variable models for clustered mixed outcomes

Citations

Cited by:

1. Maryam Aghayerashti & Ehsan Bahrami Samani & Mojtaba Ganjali, 2023. "Bayesian Latent Variable Model of Mixed Correlated Rank and Beta-Binomial Responses with Missing Data for the International Statistical Literacy Project Poster Competition," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 210-250, May.
2. David B. Dunson & M. Watson & Jack A. Taylor, 2003. "Bayesian Latent Variable Models for Median Regression on Multiple Outcomes," Biometrics, The International Biometric Society, vol. 59(2), pages 296-304, June.
3. Yang Lu, 2019. "Flexible (panel) regression models for bivariate count–continuous data with an insurance application," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1503-1521, October.
   • Yang Lu, 2019. "Flexible (panel) regression models for bivariate count-continuous data with an insurance application," Post-Print hal-02419024, HAL.
4. Che Wan Jasimah Bt Wan Mohamed Radzi & Hashem Salarzadeh Jenatabadi & Maisarah Binti Hasbullah, 2015. "Firm Sustainability Performance Index Modeling," Sustainability, MDPI, vol. 7(12), pages 1-17, December.
5. Dexen D. Z. Xi & Charmaine B. Dean & Stephen W. Taylor, 2021. "Modeling the duration and size of wildfires using joint mixture models," Environmetrics, John Wiley & Sons, Ltd., vol. 32(6), September.
6. Siliang Zhang & Yunxiao Chen, 2022. "Computation for Latent Variable Model Estimation: A Unified Stochastic Proximal Framework," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1473-1502, December.
7. Eugenia Buta & Stephanie S. O’Malley & Ralitza Gueorguieva, 2018. "Bayesian joint modelling of longitudinal data on abstinence, frequency and intensity of drinking in alcoholism trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 869-888, June.
8. Zhang, Xiao & Boscardin, W. John & Belin, Thomas R. & Wan, Xiaohai & He, Yulei & Zhang, Kui, 2015. "A Bayesian method for analyzing combinations of continuous, ordinal, and nominal categorical data with missing values," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 43-58.
9. Hoshino, Takahiro, 2008. "Bayesian significance testing and multiple comparisons from MCMC outputs," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3543-3559, March.
10. Hao Bai & Yuan Zhong & Xin Gao & Wei Xu, 2020. "Multivariate Mixed Response Model with Pairwise Composite-Likelihood Method," Stats, MDPI, vol. 3(3), pages 1-18, July.
11. Lee, Sik-Yum & Song, Xin-Yuan, 2008. "On Bayesian estimation and model comparison of an integrated structural equation model," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4814-4827, June.
12. D. B. Woodard & T. M. T. Love & S. W. Thurston & D. Ruppert & S. Sathyanarayana & S. H. Swan, 2013. "Latent factor regression models for grouped outcomes," Biometrics, The International Biometric Society, vol. 69(3), pages 785-794, September.
13. Martin Spieß, 2006. "Estimation of a Two-Equation Panel Model with Mixed Continuous and Ordered Categorical Outcomes and Missing Data," Discussion Papers 010, Europa-Universität Flensburg, International Institute of Management.
14. Xin-Yuan Song & Zhao-Hua Lu & Jing-Heng Cai & Edward Ip, 2013. "A Bayesian Modeling Approach for Generalized Semiparametric Structural Equation Models," Psychometrika, Springer;The Psychometric Society, vol. 78(4), pages 624-647, October.
15. Rungie, Cam M. & Coote, Leonard V. & Louviere, Jordan J., 2012. "Latent variables in discrete choice experiments," Journal of choice modelling, Elsevier, vol. 5(3), pages 145-156.
16. Julie S. Najita & Yi Li & Paul J. Catalano, 2009. "A novel application of a bivariate regression model for binary and continuous outcomes to studies of fetal toxicity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(4), pages 555-573, September.
17. David B. Dunson & Zhen Chen & Jean Harry, 2003. "A Bayesian Approach for Joint Modeling of Cluster Size and Subunit-Specific Outcomes," Biometrics, The International Biometric Society, vol. 59(3), pages 521-530, September.
18. Ling Zhou & Huazhen Lin & Xinyuan Song & Yi Li, 2014. "Selection of Latent Variables for Multiple Mixed-outcome Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 1064-1082, December.
19. B. N. Sánchez & E. A. Houseman & L. M. Ryan, 2009. "Residual-Based Diagnostics for Structural Equation Models," Biometrics, The International Biometric Society, vol. 65(1), pages 104-115, March.
20. Sik-Yum Lee & Xin-Yuan Song, 2004. "Maximum Likelihood Analysis of a General Latent Variable Model with Hierarchically Mixed Data," Biometrics, The International Biometric Society, vol. 60(3), pages 624-636, September.
21. Takahiro Hoshino & Ryosuke Igari, 2017. "Quasi-Bayesian Inference for Latent Variable Models with External Information: Application to generalized linear mixed models for biased data," Keio-IES Discussion Paper Series 2017-014, Institute for Economics Studies, Keio University.
22. E. Juarez‐Colunga & G. L. Silva & C. B. Dean, 2017. "Joint modeling of zero‐inflated panel count and severity outcomes," Biometrics, The International Biometric Society, vol. 73(4), pages 1413-1423, December.
23. Cai, Jing-Heng & Song, Xin-Yuan & Lam, Kwok-Hap & Ip, Edward Hak-Sing, 2011. "A mixture of generalized latent variable models for mixed mode and heterogeneous data," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2889-2907, November.
24. Martin Spieß, 2006. "On the Returns to Occupational Qualification in Terms of Subjective and Objective Variables: A GEE-type Approach to the Estimation of Two-Equation Panel Models," Discussion Papers of DIW Berlin 564, DIW Berlin, German Institute for Economic Research.
25. Hoshino, Takahiro, 2008. "A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1413-1429, January.
26. Hao Sun & Emily Berg & Zhengyuan Zhu, 2022. "Bivariate small‐area estimation for binary and gaussian variables based on a conditionally specified model," Biometrics, The International Biometric Society, vol. 78(4), pages 1555-1565, December.
27. Luo, Chongliang & Liang, Jian & Li, Gen & Wang, Fei & Zhang, Changshui & Dey, Dipak K. & Chen, Kun, 2018. "Leveraging mixed and incomplete outcomes via reduced-rank modeling," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 378-394.
28. Yang, Mingan & Dunson, David B. & Baird, Donna, 2010. "Semiparametric Bayes hierarchical models with mean and variance constraints," Computational Statistics & Data Analysis, Elsevier, vol. 54(9), pages 2172-2186, September.
29. Sally W. Thurston & David Ruppert & Philip W. Davidson, 2009. "Bayesian Models for Multiple Outcomes Nested in Domains," Biometrics, The International Biometric Society, vol. 65(4), pages 1078-1086, December.
30. Amy H. Herring & Juan Yang, 2007. "Bayesian Modeling of Multiple Episode Occurrence and Severity with a Terminating Event," Biometrics, The International Biometric Society, vol. 63(2), pages 381-388, June.
31. Chen, Hsiang-Chun & Wehrly, Thomas E., 2016. "Approximate uniform shrinkage prior for a multivariate generalized linear mixed model," Journal of Multivariate Analysis, Elsevier, vol. 145(C), pages 148-161.
32. Timo Lorenz & Cora Frischling & Raphael Cuadros & Kathrin Heinitz, 2016. "Autism and Overcoming Job Barriers: Comparing Job-Related Barriers and Possible Solutions in and outside of Autism-Specific Employment," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-19, January.
33. Hashem Salarzadeh Jenatabadi & Peyman Babashamsi & Datis Khajeheian & Nader Seyyed Amiri, 2016. "Airline Sustainability Modeling: A New Framework with Application of Bayesian Structural Equation Modeling," Sustainability, MDPI, vol. 8(11), pages 1-17, November.
34. Leila Amiri & Mojtaba Khazaei & Mojtaba Ganjali, 2018. "A mixture latent variable model for modeling mixed data in heterogeneous populations and its applications," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(1), pages 95-115, January.
35. Daniel M. McNeish, 2016. "Using Data-Dependent Priors to Mitigate Small Sample Bias in Latent Growth Models," Journal of Educational and Behavioral Statistics, , vol. 41(1), pages 27-56, February.
36. Sik-Yum Lee & Ye-Mao Xia, 2008. "A Robust Bayesian Approach for Structural Equation Models with Missing Data," Psychometrika, Springer;The Psychometric Society, vol. 73(3), pages 343-364, September.
37. Zhang, Siliang & Chen, Yunxiao, 2022. "Computation for latent variable model estimation: a unified stochastic proximal framework," LSE Research Online Documents on Economics 114489, London School of Economics and Political Science, LSE Library.
38. Yuda Zhu & Robert E. Weiss, 2013. "Modeling Seroadaptation and Sexual Behavior Among HIV-super-+ Study Participants with a Simultaneously Multilevel and Multivariate Longitudinal Count Model," Biometrics, The International Biometric Society, vol. 69(1), pages 214-224, March.
39. Dingjing Shi & Xin Tong, 2017. "The Impact of Prior Information on Bayesian Latent Basis Growth Model Estimation," SAGE Open, , vol. 7(3), pages 21582440177, August.
40. Li Cai, 2010. "High-dimensional Exploratory Item Factor Analysis by A Metropolis–Hastings Robbins–Monro Algorithm," Psychometrika, Springer;The Psychometric Society, vol. 75(1), pages 33-57, March.
41. Assaf, A. George & Tsionas, Mike & Oh, Haemoon, 2018. "The time has come: Toward Bayesian SEM estimation in tourism research," Tourism Management, Elsevier, vol. 64(C), pages 98-109.
42. Li, Yun-Xian & Kano, Yutaka & Pan, Jun-Hao & Song, Xin-Yuan, 2012. "A criterion-based model comparison statistic for structural equation models with heterogeneous data," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 92-107.
43. Cécile Proust & Hélène Jacqmin-Gadda & Jeremy M. G. Taylor & Julien Ganiayre & Daniel Commenges, 2006. "A Nonlinear Model with Latent Process for Cognitive Evolution Using Multivariate Longitudinal Data," Biometrics, The International Biometric Society, vol. 62(4), pages 1014-1024, December.
44. Lu, Zhenqiu (Laura) & Zhang, Zhiyong, 2014. "Robust growth mixture models with non-ignorable missingness: Models, estimation, selection, and application," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 220-240.
45. H. Zhang & Q. Yu & C. Feng & D. Gunzler & P. Wu & X. M. Tu, 2012. "A new look at the difference between the GEE and the GLMM when modeling longitudinal count responses," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(9), pages 2067-2079, June.
46. Ralitza V. Gueorguieva, 2005. "Comments about Joint Modeling of Cluster Size and Binary and Continuous Subunit-Specific Outcomes," Biometrics, The International Biometric Society, vol. 61(3), pages 862-866, September.
47. Hashem Salarzadeh Jenatabadi & Sedigheh Moghavvemi & Che Wan Jasimah Bt Wan Mohamed Radzi & Parastoo Babashamsi & Mohammad Arashi, 2017. "Testing students’ e-learning via Facebook through Bayesian structural equation modeling," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-19, September.
48. David B. Dunson & Donna D. Baird, 2002. "Bayesian Modeling of Incidence and Progression of Disease from Cross-Sectional Data," Biometrics, The International Biometric Society, vol. 58(4), pages 813-822, December.
49. Ali Noudoostbeni & Kiran Kaur & Hashem Salarzadeh Jenatabadi, 2018. "A Comparison of Structural Equation Modeling Approaches with DeLone & McLean’s Model: A Case Study of Radio-Frequency Identification User Satisfaction in Malaysian University Libraries," Sustainability, MDPI, vol. 10(7), pages 1-16, July.