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Generalized latent trait models

Citations

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Cited by:

  1. 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.
  2. Domenico Piccolo & Rosaria Simone, 2019. "The class of cub models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 389-435, September.
  3. J. Zhu & J. C. Eickhoff & P. Yan, 2005. "Generalized Linear Latent Variable Models for Repeated Measures of Spatially Correlated Multivariate Data," Biometrics, The International Biometric Society, vol. 61(3), pages 674-683, September.
  4. Wang, Fa, 2017. "Maximum likelihood estimation and inference for high dimensional nonlinear factor models with application to factor-augmented regressions," MPRA Paper 93484, University Library of Munich, Germany, revised 19 May 2019.
  5. Yanyuan Ma & Marc G. Genton, 2010. "Explicit estimating equations for semiparametric generalized linear latent variable models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 475-495, September.
  6. 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.
  7. Antoinette Nneka Opara & Joyzy Pius Egunjobi, 2023. "Validating the Instrument, Egunjobi’s Child Response Style Scale (CReSS)," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 7(12), pages 1813-1825, December.
  8. David B. Dunson & Sally D. Perreault, 2001. "Factor Analytic Models of Clustered Multivariate Data with Informative Censoring," Biometrics, The International Biometric Society, vol. 57(1), pages 302-308, March.
  9. 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.
  10. Dylan Molenaar, 2015. "Heteroscedastic Latent Trait Models for Dichotomous Data," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 625-644, September.
  11. 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.
  12. Shing-On Leung, 2008. "A Three-Dimensional Latent Variable Model for Attitude Scales," Sociological Methods & Research, , vol. 37(1), pages 135-154, August.
  13. Yingchao Zhong & Chang Wang & Lu Wang, 2021. "Survival Augmented Patient Preference Incorporated Reinforcement Learning to Evaluate Tailoring Variables for Personalized Healthcare," Stats, MDPI, vol. 4(4), pages 1-17, September.
  14. Vitoratou, Silia & Ntzoufras, Ioannis & Moustaki, Irini, 2016. "Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions," LSE Research Online Documents on Economics 57685, London School of Economics and Political Science, LSE Library.
  15. Alexander Weissman, 2013. "Global Convergence of the EM Algorithm for Unconstrained Latent Variable Models with Categorical Indicators," Psychometrika, Springer;The Psychometric Society, vol. 78(1), pages 134-153, January.
  16. 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.
  17. Zheng-Yun Zhuang & Chi-Kit Ho & Paul Juinn Bing Tan & Jia-Ming Ying & Jin-Hua Chen, 2020. "The Optimal Setting of A/B Exam Papers without Item Pools: A Hybrid Approach of IRT and BGP," Mathematics, MDPI, vol. 8(8), pages 1-29, August.
  18. Tsonaka, R. & Moustaki, I., 2007. "Parameter constraints in generalized linear latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4164-4177, May.
  19. Robin Fuchs & Denys Pommeret & Cinzia Viroli, 2022. "Mixed Deep Gaussian Mixture Model: a clustering model for mixed datasets," 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. 16(1), pages 31-53, March.
  20. Xia, Ye-Mao & Tang, Nian-Sheng & Gou, Jian-Wei, 2016. "Generalized linear latent models for multivariate longitudinal measurements mixed with hidden Markov models," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 259-275.
  21. Silvia Cagnone & Cinzia Viroli, 2014. "A factor mixture model for analyzing heterogeneity and cognitive structure of dementia," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(1), pages 1-20, January.
  22. Michel Wedel & Wagner Kamakura, 2001. "Factor analysis with (mixed) observed and latent variables in the exponential family," Psychometrika, Springer;The Psychometric Society, vol. 66(4), pages 515-530, December.
  23. Bianconcini, Silvia & Cagnone, Silvia, 2012. "Estimation of generalized linear latent variable models via fully exponential Laplace approximation," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 183-193.
  24. Piotr Tarka, 2013. "Model of latent profile factor analysis for ordered categorical data," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 14(1), pages 171-182, March.
  25. Zhang, Q. & Ip, E.H., 2014. "Variable assessment in latent class models," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 146-156.
  26. An, Xinming & Bentler, Peter M., 2012. "Efficient direct sampling MCEM algorithm for latent variable models with binary responses," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 231-244.
  27. Nicholas J. Rockwood, 2021. "Efficient Likelihood Estimation of Generalized Structural Equation Models with a Mix of Normal and Nonnormal Responses," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 642-667, June.
  28. Silvia Cagnone & Cinzia Viroli, 2018. "Multivariate latent variable transition models of longitudinal mixed data: an analysis on alcohol use disorder," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1399-1418, November.
  29. 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.
  30. Silvia Cagnone & Paola Monari, 2013. "Latent variable models for ordinal data by using the adaptive quadrature approximation," Computational Statistics, Springer, vol. 28(2), pages 597-619, April.
  31. Fithian, William & Josse, Julie, 2017. "Multiple correspondence analysis and the multilogit bilinear model," Journal of Multivariate Analysis, Elsevier, vol. 157(C), pages 87-102.
  32. 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.
  33. 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.
  34. Isabella Sulis & Mariano Porcu, 2012. "Comparing degree programs from students’ assessments: A LCRA-based adjusted composite indicator," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(2), pages 193-209, June.
  35. Matthias von Davier, 2021. "Commentary: Matching IRT Models to PRO Constructs— Modeling Alternatives, and Some Thoughts on What Makes a Model Different," Psychometrika, Springer;The Psychometric Society, vol. 86(3), pages 825-832, September.
  36. Wang, Fa, 2022. "Maximum likelihood estimation and inference for high dimensional generalized factor models with application to factor-augmented regressions," Journal of Econometrics, Elsevier, vol. 229(1), pages 180-200.
  37. Qian, Zhiguang & Shapiro, Alexander, 2006. "Simulation-based approach to estimation of latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1243-1259, November.
  38. Minjeong Jeon & Paul Boeck & Jevan Luo & Xiangrui Li & Zhong-Lin Lu, 2021. "Modeling Within-Item Dependencies in Parallel Data on Test Responses and Brain Activation," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 239-271, March.
  39. Milo Vandemoortele, 2014. "Measuring Household Wealth with Latent Trait Modelling: An Application to Malawian DHS Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 118(2), pages 877-891, September.
  40. Ting Fung Ma & Fangfang Wang & Jun Zhu, 2023. "On generalized latent factor modeling and inference for high‐dimensional binomial data," Biometrics, The International Biometric Society, vol. 79(3), pages 2311-2320, September.
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