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Maximum likelihood estimation for mixed continuous and categorical data with missing values
Abstract
Maximum likelihood procedures for analysing mixed continuous and categorical data with missing values are presented. The general location model of 01kin & Tate (1961) and extensions introduced by Krzanowski (1980, 1982) form the basis for our methods. Maximum likelihood estimation with incomplete data is achieved by an application of the EM algorithm (Dempster, Laird & Rubin, 1977). Special cases of the algorithm include Orchard & Woodbury's (1972) algorithm for incomplete normal samples, Fuchs's (1982) algorithms for log linear modelling of partially classified contingency tables, and Day's (1969) algorithm for multivariate normal mixtures. Applications include: (a) imputation of missing values, (b) logistic regression and discriminant analysis with missing predictors and unclassified observations, (c) linear regression with missing continuous and categorical predictors, and (d) parametric cluster analysis with incomplete data. Methods are illustrated using data from the St Louis Risk Research Project.
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Cited by
MissForest—Non-parametric missing value imputation for mixed-type data
Stekhoven, D. J., & Bühlmann, P. (2012) · Bioinformatics · doi:10.1093/bioinformatics/btr597 · Cited by 6,156
“Little and Schluchter (1985) presented an approach based on maximum likelihood estimation combining the multivariate normal model for continuous and the Poisson/multinomial model for categorical data.”
MethodModeling the Drop-Out Mechanism in Repeated-Measures Studies
Little, R. J. A. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476615 · Cited by 1,522
Self-citationThe EM algorithm for graphical association models with missing data
Lauritzen, S. L. (1995) · Computational Statistics & Data Analysis · doi:10.1016/0167-9473(93)e0056-a · Cited by 760
What Do We Do with Missing Data? Some Options for Analysis of Incomplete Data
Raghunathan, T. E. (2004) · Annual Review of Public Health · doi:10.1146/annurev.publhealth.25.102802.124410 · Cited by 429
Incomplete Data in Generalized Linear Models
Ibrahim, J. G. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10474938 · Cited by 344
Metadata
- Authors
- LITTLE, R. J. A., & SCHLUCHTER, M. D.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1985
- Volume
- 72
- Issue
- 3
- Pages
- 497-512
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2013522567
- Cited by
- 246
- References
- 0
- FWCI
- 1.71
- Type
- journal article
- Language
- en
- Open access
- closed
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- LITTLE, R. J. A., & SCHLUCHTER, M. D. (1985). Maximum likelihood estimation for mixed continuous and categorical data with missing values. Biometrika. https://doi.org/10.1093/biomet/72.3.497
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