[1] E. Bahrami Samani and M. Ganjali, Bayesian latent variable model for mixed continuous and ordinal responses with possibility of missing responses, Journal of Applied Statistics, 38 (2011), pp. 1103–1116.
[2] E. Bahrami Samani and M. Ganjali, Mixed correlated bivariate ordinal and negative binomial longitudinal responses with nonignorable missing values, Communications in Statistics-Theory and Methods, 43 (2014), pp. 2659–2673.
[3] B. P. Berg, Location models in healthcare, in Handbook of Healthcare Operations Management: Methods and Applications, Springer, 2013, pp. 387–402.
[4] D. R. Cox, The analysis of multivariate binary data, J. R. Statist. Soc., Ser. C (Applied Statistics), 21 (1972), pp. 113–120.
[5] D. R. Cox and N. Wermuth, Response models for mixed binary and quantitative variables, Biometrika, 79 (1992), pp. 441–461.
[6] A. R. de Leon and K. C. Carri´ere, On the one-sample location hypothesis for mixed bivariate data, Commun. Statist. Theory Methods, 29 (2000), pp. 2573–2581.
[7] A. R. de Leon and K. C. Carri`ere, General mixed-data model: extension of general location and grouped continuous models, Canad. J. Statist., 35 (2007), pp. 533–548.
[8] D. B. Dunson, Bayesian latent variable models for clustered mixed outcomes, J. R. Statist. Soc. Ser. B, (Statistical Methodology), 62 (2000), pp. 355–366.
[9] K. E. Ensrud and C. J. Crandall, Osteoporosis, Ann. Intern. Med., 167 (2017), pp. ITC17–ITC32.
[10] J. L´opez-Fidalgo and W. Wong, Optimal designs for correlated data, Annual Review of Statistics and Its Application, 13 (2025).
[11] H. Mogouie, G. A. Raissi Ardali, E. Bahrami Samani, and A. Amiri, Statistical monitoring of binary response attributed social networks considering random effects, Communications in Statistics-Simulation and Computation, 51 (2020), pp. 973–992.
[12] S. Nomura, A. Kitami, R. Takao-Kawabata, A. Takakura, M. Nakatsugawa, R. Kono, A. Maeno, A. Tokuda, Y. Isogai, T. Ishizuya, et al., Teriparatide improves bone and lipid metabolism in a male rat model of type 2 diabetes mellitus, Endocrinology, 160 (2019), pp. 2339–2352.
[13] I. Olkin and R. F. Tate, Multivariate correlation models with mixed discrete and continuous variables, Ann. Math. Statist., 32 (1961), pp. 743–453.
[14] A. J. Onwuegbuzie, J. R. Slate, N. L. Leech, and K. M. Collins, Mixed data analysis: Advanced integration techniques, International Journal of Multiple Research Approaches, 3 (2009), pp. 13–33.
[15] W.-Y. Poon and S.-Y. Lee, Maximum likelihood estimation of multivariate polyserial and polychoric corre- lation coefficients, Psychometrika, 52 (1987), pp. 409–430.
[16] M. D. Sammel, L. M. Ryan, and J. M. Legler, Latent variable models for mixed discrete and continuous outcomes, J. R. Statist. Soc. Ser. B, (Methodological), 59 (1997), pp. 667–678.
[17] G. Sen, M. Krishnamoorthy, N. Rangaraj, and V. Narayanan, Facility location models to locate data in information networks: a literature review, Annals of Operations Research, 246 (2016), pp. 313–348.
[18] W. W. Stroup, M. Ptukhina, and J. Garai, Generalized linear mixed models: modern concepts, methods and applications, Chapman and Hall/CRC, 2024.
[19] Y. Zhang, S. Q. Xie, H. Wang, and Z. Zhang, Data analytics in steady-state visual evoked potential-based brain–computer interface: A review, IEEE Sensors Journal, 21 (2020), pp. 1124–1138.
[20] L. Zhou, H. Lin, X. Song, and Y. Li, Selection of latent variables for multiple mixed-outcome models, Scandinavian Journal of Statistics, 41 (2014), pp. 1064–1082.
