References
- Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16, 397–438. https://doi.org/10.1080/10705510903008204
- Auerswald, M., & Moshagen, M. (2019). How to determine the number of factors to retain in exploratory factor analysis: A comparison of extraction methods under realistic conditions. Psychological Methods, 24, 468–491. https://doi.org/10.1037/met0000200
- Bernaards, C. A., & Jennrich, R. I. (2005). Gradient projection algorithms and software for arbitrary rotation criteria in factor analysis. Educational and Psychological Measurement, 65, 676–696. https://doi.org/10.1177/0013164404272507
- Box, G. E. P., & Tiao, G. C. (1973). Bayesian inference in statistical analysis. Addison-Wesley.
- Browne, M. W. (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36, 111–150. https://doi.org/10.1207/S15327906MBR3601_05
- Casella, G. (2001). Empirical Bayes Gibbs sampling. Biostatistics, 2, 485–500. https://doi.org/10.1093/biostatistics/2.4.485
- Casella, G., & George, E. I. (1992). Explaining the Gibbs sampler. The American Statistician, 46, 167–174. https://doi.org/10.1080/00031305.1992.10475878
- Conti, G., Frühwirth-Schnatter, S., Heckman, J. J., & Piatek, R. (2014). Bayesian exploratory factor analysis. Journal of Econometrics, 183, 31–57. https://doi.org/10.1016/j.jeconom.2014.06.008
- Feng, X.-N., Wu, H.-T., & Song, X.-Y. (2017). Bayesian regularized multivariate generalized latent variable models. Structural Equation Modeling, 24, 341–358. https://doi.org/10.1080/10705511.2016.1257353
- Frühwirth-Schnatter, S., & Lopes, H. F. (2018). Sparse Bayesian factor analysis when the number of factors is unknown. arXiv.org. https://arxiv.org/abs/1804.04231
- Gelman, A. (1996). Inference and monitoring convergence. In W. R. Gilks, S. Richardson, & D. J. Spiegelharter (Eds.), Markov chain Monte Carlo in practice (pp. 131–144). Chapman & Hall.
- Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2004). Bayesian data analysis (2nd ed.). Chapman & Hall.
- Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721–741. https://doi.org/10.1109/TPAMI.1984.4767596
- Gilks, W. R., Richardson, S., & Spiegelhalter, D. J. (Eds). (1996). Markov chain Monte Carlo in practice. Chapman & Hall.
- Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their application. Biometrika, 57, 97–109. https://doi.org/10.1093/biomet/57.1.97
- Heintz, S. (2017). Putting a spotlight on daily humor behaviors: Dimensionality and relationships with personality, subjective well-being, and humor styles. Personality and Individual Differences, 104, 407–412. https://doi.org/10.1016/j.paid.2016.08.042
- Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179–185. https://doi.org/10.1007/BF02289447
- Huang, P.-H. (2018). A penalized likelihood method for multi-group structural equation modelling. British Journal of Mathematical and Statistical Psychology, 71, 499–522. https://doi.org/10.1111/bmsp.12130
- Huang, P.-H., Chen, H., & Weng, L.-J. (2017). A penalized likelihood method for structural equation modeling. Psychometrika, 82, 329–354. https://doi.org/10.1007/s11336-017-9566-9
- Jacobucci, R., Grimm, K. J., & McArdle, J. J. (2016). Regularized structural equation modeling. Structural Equation Modeling, 23, 555–566. https://doi.org/10.1080/10705511.2016.1154793
- Liu, X. (2008). Parameter expansion for sampling a correlation matrix: An efficient GPX-RPMH algorithm. Journal of Statistical Computation and Simulation, 78, 1065–1076. https://doi.org/10.1080/00949650701519635
- Liu, X., & Daniels, M. J. (2006). A new efficient algorithm for sampling a correlation matrix based on parameter expansion and re-parameterization. Journal of Computational and Graphical Statistics, 15, 897–914. https://doi.org/10.1198/106186006X160681
- Lu, Z. H., Chow, S. M., & Loken, E. (2016). Bayesian factor analysis as a variable-selection problem: Alternative priors and consequences. Multivariate Behavioral Research, 51, 519–539. https://doi.org/10.1080/00273171.2016.1168279
- Martin, R. A., Puhlik-Doris, P., Larsen, G., Gray, J., & Weir, K. (2003). Individual differences in uses of humor and their relation to psychological well-being: Development of the humor styles questionnaire. Journal of Research in Personality, 37, 48–75. https://doi.org/10.1016/S0092-6566(02)00534-2
- Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equations of state calculations by fast computing machine. Journal of Chemical Physics, 21, 1087–1091. https://doi.org/10.1063/1.1699114
- Mulaik, S. A. (2010). Foundations of factor analysis (2nd ed.). Chapman & Hall/CRC.
- Muthén, B. O., & Asparouhov, T. (2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17, 313–335. https://doi.org/10.1037/a0026802
- Papastamoulis, P. (2020). Clustering multivariate data using factor analytic Bayesian mixtures with an unknown number of components. Statistics and Computing, 30, 485–506. https://doi.org/10.1007/s11222-019-09891-z
- Park, T., & Casella, G. (2008). The Bayesian lasso. Journal of the American Statistical Association, 103, 681–686. https://doi.org/10.1198/016214508000000337
- Piatek, R. (2020). BayesFM: Bayesian inference for factor modeling (R package version 0.1.4). CRAN. https://CRAN.R-project.org/package=BayesFM
- Plummer, M., Best, N., Cowles, K., & Vines, K. (2006). CODA: Convergence diagnosis and output analysis for MCMC. R News, 6, 7–11. https://cran.r-project.org/doc/Rnews/Rnews_2006-1.pdf#page=7
- R Development Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. http://www.R-project.org
- Revelle, W. (2020). psych: Procedures for psychological, psychometric, and personality research (R package version 2.0.9). CRAN. https://CRAN.R-project.org/package=psych
- Sass, D. A., & Schmitt, T. A. (2010). A comparative investigation of rotation criteria within exploratory factor analysis. Multivariate Behavioral Research, 45, 73–103. https://doi.org/10.1080/00273170903504810
- Scharf, F., & Nestler, S. (2019). Should regularization replace simple structure rotation in exploratory factor analysis? Structural Equation Modeling, 26, 576–590. https://doi.org/10.1080/10705511.2018.1558060
- Simon, N., Friedman, J., Hastie, T., & Tibshirani, R. (2013). A sparse-group Lasso. Journal of Computational and Graphical Statistics, 22, 231–245. https://doi.org/10.1080/10618600.2012.681250
- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B, 58, 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
- Trendafilov, N. T., Fontanella, S., & Adachi, K. (2017). Sparse exploratory factor analysis. Psychometrika, 82, 778–794. https://doi.org/10.1007/s11336-017-9575-8
- Van Erp, S., Mulder, J., & Oberski, D. L. (2017). Prior sensitivity analysis in default Bayesian structural equation modeling. Psychological Methods, 23, 363–388. https://doi.org/10.1037/met0000162
- Xu, X., & Ghosh, M. (2015). Bayesian variable selection and estimation for group Lasso. Bayesian Analysis, 10, 909–936. https://doi.org/10.1214/14-BA929
- Yates, A. (1987). Multivariate exploratory data analysis: A perspective on exploratory factor analysis. State Univ. of New York Press.
- Yuan, M., & Lin, Y. (2005). Efficient empirical Bayes variable selection and estimation in linear models. Journal of the American Statistical Association, 100, 1215–1225. https://doi.org/10.1198/016214505000000367
- Yuan, M., & Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68, 49–67. https://doi.org/10.1111/j.1467-9868.2005.00532.x