The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Latent Variable Models for Mixed Discrete and Continuous Outcomes
Abstract
Summary: We propose a latent variable model for mixed discrete and continuous outcomes. The model accommodates any mixture of outcomes from an exponential family and allows for arbitrary covariate effects, as well as direct modelling of covariates on the latent variable. An EM algorithm is proposed for parameter estimation and estimates of the latent variables are produced as a by-product of the analysis. A generalized likelihood ratio test can be used to test the significance of covariates affecting the latent outcomes. This method is applied to birth defects data, where the outcomes of interest are continuous measures of size and binary indicators of minor physical anomalies. Infants who were exposed in utero to anticonvulsant medications are compared with controls.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
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LIANG, K.-Y., & ZEGER, S. L. (1986) · Biometrika · doi:10.1093/biomet/73.1.13 · Cited by 18,550
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Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,265
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Longitudinal Data Analysis for Discrete and Continuous Outcomes
Zeger, S. L., & Liang, K.-Y. (1986) · Biometrics · doi:10.2307/2531248 · Cited by 7,764
Agresti, A. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/0471249688 · Cited by 6,746
Generalized Linear Models (2nd ed.).
Therneau, T. M., McCullagh, P., & Nelder, J. A. (1993) · Journal of the American Statistical Association · doi:10.2307/2290358 · Cited by 4,907
Approximate Inference in Generalized Linear Mixed Models
Breslow, N. E., & Clayton, D. G. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10594284 · Cited by 4,629
Maximum likelihood estimation via the ECM algorithm: A general framework
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Cited by
Nguena Nguefack, H. L., Pagé, M. G., Katz, J., Choinière, M., Vanasse, A., Dorais, M., Samb, O. M., & Lacasse, A. (2020) · Clinical Epidemiology · doi:10.2147/clep.s265287 · Cited by 527
Bayesian Latent Variable Models for Clustered Mixed Outcomes
Dunson, D. B. (2000) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00236 · Cited by 305
Generalized Latent Trait Models
Moustaki, I., & Knott, M. (2000) · Psychometrika · doi:10.1007/bf02296153 · Cited by 254
Lambert, P., & Vandenhende, F. (2002) · Statistics in Medicine · doi:10.1002/sim.1249 · Cited by 86
Vickerstaff, V., Ambler, G., & Omar, R. Z. (2021) · Biometrical Journal · doi:10.1002/bimj.201900040 · Cited by 26
Metadata
- Authors
- Sammel, M. D., Ryan, L. M., & Legler, J. M.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 1997-09-01
- Volume
- 59
- Issue
- 3
- Pages
- 667-678
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2150983145
- Cited by
- 313
- References
- 31
- FWCI
- 6.18
- Type
- journal article
- Language
- en
- Open access
- closed
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- Sammel, M. D., Ryan, L. M., & Legler, J. M. (1997). Latent Variable Models for Mixed Discrete and Continuous Outcomes. Journal of the Royal Statistical Society Series B: Statistical Methodology. https://doi.org/10.1111/1467-9868.00090
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
