Model Search in Contingency Tables by CoCo. CoCo, a highly advanced program for analysis of complete and incomplete contingency tables, is presented. In the paper a short presentation of CoCo is given. Incremental search by backward elimination and forward selection and the global search procedure from Edwards & Havránek (1985) is considered. By incremental search a single minimal acceptable model is identified. By the principles of weakly accepted and weakly rejected the class of minimal acceptable models are found in the global search procedure. In CoCo each of the model searches can be done by a single command, or CoCo can be guided through the search in a highly user controlled model selection.
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References in zbMATH (referenced in 8 articles , 1 standard article )
Showing results 1 to 8 of 8.
- Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
- Luca Scrucca; Adrian Raftery: clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R (2018) not zbMATH
- Soren Hojsgaard: YGGDRASIL - A Statistical Package for Learning Split Models (2013) arXiv
- Dean, Nema; Raftery, Adrian E.: Latent class analysis variable selection (2010)
- Murphy, Thomas Brendan; Dean, Nema; Raftery, Adrian E.: Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications (2010)
- Drton, Mathias; Perlman, Michael D.: Multiple testing and error control in Gaussian graphical model selection (2007)
- Cheng, Jie; Greiner, Russell; Kelly, Jonathan; Bell, David; Liu, Weiru: Learning Bayesian networks from data: An information-theory based approach (2002)
- Jens Badsberg: A Guide to CoCo (2001) not zbMATH