A Latent Class Model with Hidden Markov Dependence for Array CGH Data
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Publication:5850980
DOI10.1111/j.1541-0420.2009.01226.xzbMath1180.62164OpenAlexW2080416780WikidataQ33435567 ScholiaQ33435567MaRDI QIDQ5850980
Rebecca A. Betensky, David N. Louis, Andres Houseman, Stacia M. DeSantis, Gayatry Mohapatra, Brent A. Coull
Publication date: 21 January 2010
Published in: Biometrics (Search for Journal in Brave)
Full work available at URL: http://europepmc.org/articles/pmc3052263
Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Medical applications (general) (92C50)
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Uses Software
Cites Work
- Hidden Markov models approach to the analysis of array CGH data
- Generalized functional linear models
- Functional Clustering by Bayesian Wavelet Methods
- A pseudolikelihood approach for simultaneous analysis of array comparative genomic hybridizations
- Joint Analysis of Time‐to‐Event and Multiple Binary Indicators of Latent Classes
- Bayesian Hidden Markov Modeling of Array CGH Data
- Bayesian Forecasting of Multinomial Time Series Through Conditional Gaussian Dynamic Models
- Generalized Linear Models with Functional Predictors
- Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)
- Deviance information criteria for missing data models
