The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Practical Bayesian Density Estimation Using Mixtures of Normals
Abstract
Mixtures of normals provide a flexible model for estimating densities in a Bayesian framework. There are some difficulties with this model, however. First, standard reference priors yield improper posteriors. Second, the posterior for the number of components in the mixture is not well defined (if the reference prior is used). Third, posterior simulation does not provide a direct estimate of the posterior for the number of components. We present some practical methods for coping with these problems. Finally, we give some results on the consistency of the method when the maximum number of components is allowed to grow with the sample size.
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Cites
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
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Markov Chain Monte Carlo in Practice
(1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835 · Cited by 7,757
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GREEN, P. J. (1995) · Biometrika · doi:10.1093/biomet/82.4.711 · Cited by 5,930
A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the sum of Observations
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Markov Chains for Exploring Posterior Distributions
Tierney, L. (1994) · The Annals of Statistics · doi:10.1214/aos/1176325750 · Cited by 3,452
Bayesian Density Estimation and Inference Using Mixtures
Escobar, M. D., & West, M. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476550 · Cited by 2,255
Unmasking Multivariate Outliers and Leverage Points
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Cited by
Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705510701575396 · Cited by 11,415
McLachlan, G., & Peel, D. (2000) · Wiley Series in Probability and Statistics · doi:10.1002/0471721182 · Cited by 10,991
Assessing a mixture model for clustering with the integrated completed likelihood
Biernacki, C., Celeux, G., & Govaert, G. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.865189 · Cited by 1,535
Bauer, D. J., & Curran, P. J. (2004) · Psychological Methods · doi:10.1037/1082-989x.9.1.3 · Cited by 536
Modeling Uncertainty in Latent Class Membership: A Case Study in Criminology
Roeder, K., Lynch, K. G., & Nagin, D. S. (1999) · Journal of the American Statistical Association · doi:10.1080/01621459.1999.10474179 · Cited by 342
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Metadata
- Authors
- Roeder, K., & Wasserman, L.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1997-09
- Volume
- 92
- Issue
- 439
- Pages
- 894-902
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2141770651
- Cited by
- 503
- References
- 23
- FWCI
- 18.07
- Type
- journal article
- Language
- en
- Open access
- closed
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