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Latent Variable Models for Mixed Discrete and Continuous Outcomes

Citations

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Cited by:

  1. Feng, Xiangnan & Lu, Bin & Song, Xinyuan & Ma, Shuang, 2019. "Financial literacy and household finances: A Bayesian two-part latent variable modeling approach," Journal of Empirical Finance, Elsevier, vol. 51(C), pages 119-137.
  2. Celine Marielle Laffont & Marc Vandemeulebroecke & Didier Concordet, 2014. "Multivariate Analysis of Longitudinal Ordinal Data With Mixed Effects Models, With Application to Clinical Outcomes in Osteoarthritis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 955-966, September.
  3. 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.
  4. 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.
  5. Ling Zhou & Huazhen Lin & Yi-Chen Lin, 2016. "Education, Intelligence, and Well-Being: Evidence from a Semiparametric Latent Variable Transformation Model for Multiple Outcomes of Mixed Types," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 125(3), pages 1011-1033, February.
  6. Fateme Rezaee & Ehsan Bahrami Samani, 2026. "Gaussian Copula Joint Models for Mixed Longitudinal (k, l)-Inflated Conway–Maxwell Poisson and Continuous Responses," Annals of Data Science, Springer, vol. 13(1), pages 41-62, February.
  7. Peter Congdon, 2010. "A multiple indicator, multiple cause method for representing social capital with an application to psychological distress," Journal of Geographical Systems, Springer, vol. 12(1), pages 1-23, March.
  8. 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.
  9. Mieke Beth Thomeer & Rin Reczek & Lawrence Stacey, 2022. "Childbearing Biographies as a Method to Examine Diversity and Clustering of Childbearing Experiences: A Research Brief," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(4), pages 1405-1415, August.
  10. 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.
  11. Zhenzhen Zhang & Thomas M. Braun & Karen E. Peterson & Howard Hu & Martha M. Téllez-Rojo & Brisa N. Sánchez, 2018. "Extending Tests of Random Effects to Assess for Measurement Invariance in Factor Models," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(3), pages 634-650, December.
  12. 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.
  13. Fokoué, Ernest, 2005. "Mixtures of factor analyzers: an extension with covariates," Journal of Multivariate Analysis, Elsevier, vol. 95(2), pages 370-384, August.
  14. Jun Zhu & Jens C. Eickhoff & Mark S. Kaiser, 2003. "Modeling the Dependence between Number of Trials and Success Probability in Beta-Binomial–Poisson Mixture Distributions," Biometrics, The International Biometric Society, vol. 59(4), pages 955-961, December.
  15. 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.
  16. Hoshino, Takahiro, 2008. "Bayesian significance testing and multiple comparisons from MCMC outputs," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3543-3559, March.
  17. 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.
  18. Sik-Yum Lee & Xin-Yuan Song, 2007. "A Unified Maximum Likelihood Approach for Analyzing Structural Equation Models With Missing Nonstandard Data," Sociological Methods & Research, , vol. 35(3), pages 352-381, February.
  19. Chen Yuqi & Guo Wensheng & Kotanko Peter & Usvyat Len & Wang Yuedong, 2016. "Joint Model for Mortality and Hospitalization," The International Journal of Biostatistics, De Gruyter, vol. 12(2), pages 1-11, November.
  20. Samson B. Adebayo & Ludwig Fahrmeir & Christian Seiler & Christian Heumann, 2011. "Geoadditive Latent Variable Modeling of Count Data on Multiple Sexual Partnering in Nigeria," Biometrics, The International Biometric Society, vol. 67(2), pages 620-628, June.
  21. 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.
  22. 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.
  23. Peter M. Fayers & David J. Hand, 2002. "Causal variables, indicator variables and measurement scales: an example from quality of life," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(2), pages 233-253, June.
  24. Bayerstadler, Andreas & van Dijk, Linda & Winter, Fabian, 2016. "Bayesian multinomial latent variable modeling for fraud and abuse detection in health insurance," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 244-252.
  25. Jenni Niku & David I. Warton & Francis K. C. Hui & Sara Taskinen, 2017. "Generalized Linear Latent Variable Models for Multivariate Count and Biomass Data in Ecology," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(4), pages 498-522, December.
  26. Nussbaum, Frank & Giesen, Joachim, 2020. "Pairwise sparse + low-rank models for variables of mixed type," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
  27. Roy Surupa & Banerjee, Tathagata, 2007. "Analysis of Mixed Outcomes: Misclassified Binary Responses and Measurement Error in Covariates," IIMA Working Papers WP2007-01-08, Indian Institute of Management Ahmedabad, Research and Publication Department.
  28. Song, Xin-Yuan & Lee, Sik-Yum, 2002. "A Bayesian model selection method with applications," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 539-557, September.
  29. Zhang, Q. & Ip, E.H., 2014. "Variable assessment in latent class models," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 146-156.
  30. Jane Osburn, 2011. "A Latent Variable Approach to Examining the Effects of HR Policies on the Inter- and Intra-Establishment Wage and Employment Structure: A Study of Two Precision Manufacturing Industries," Working Papers 451, U.S. Bureau of Labor Statistics.
  31. Emilio Augusto Coelho-Barros & Jorge Alberto Achcar & Josmar Mazucheli, 2010. "Longitudinal Poisson modeling: an application for CD4 counting in HIV-infected patients," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 865-880.
  32. Asokan Mulayath Variyath & Anita Brobbey, 2020. "Variable selection in multivariate multiple regression," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-15, July.
  33. Alessandra Guglielmi & Francesca Ieva & Anna Maria Paganoni & Fernardo A. Quintana, 2018. "A semiparametric Bayesian joint model for multiple mixed-type outcomes: an application to acute myocardial infarction," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(2), pages 399-423, June.
  34. D. J. Bartholomew, 2002. "Discussion on the paper by Fayers and Hand," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(2), pages 253-261, June.
  35. Julie S. Najita & Paul J. Catalano, 2013. "On Determining the BMD from Multiple Outcomes in Developmental Toxicity Studies when One Outcome is Intentionally Missing," Risk Analysis, John Wiley & Sons, vol. 33(8), pages 1500-1509, August.
  36. Takahiro Hoshino & Hiroshi Kurata & Kazuo Shigemasu, 2006. "A Propensity Score Adjustment for Multiple Group Structural Equation Modeling," Psychometrika, Springer;The Psychometric Society, vol. 71(4), pages 691-712, December.
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