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  "Title": "Bayesian Effect Fusion for Categorical Predictors",
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  "Description": "Variable selection and Bayesian effect fusion for\ncategorical predictors in linear and logistic regression\nmodels. Effect fusion aims at the question which categories\nhave a similar effect on the response and therefore can be\nfused to obtain a sparser representation of the model. Effect\nfusion and variable selection can be obtained either with a\nprior that has an interpretation as spike and slab prior on the\nlevel effect differences or with a sparse finite mixture prior\non the level effects. The regression coefficients are estimated\nwith a flat uninformative prior after model selection or by\ntaking model averages. Posterior inference is accomplished by\nan MCMC sampling scheme which makes use of a data augmentation\nstrategy (Polson, Scott & Windle (2013)\n<doi:10.1080/01621459.2013.829001>) based on latent Polya-Gamma\nrandom variables in the case of logistic regression. The code\nfor data augmentation is taken from Polson et al. (2013)\n<doi:10.1080/01621459.2013.829001>, who own the copyright.\nCompatibility updates by Renova.",
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