Exploration of Empirical-Bayes Hierarchical Modelling for the Analysis of Genome-Wide Association Study Data
نویسندگان
چکیده
1 Mixture Model and Bayesian Model Interpretations Combining Equations 2.1 and 2.2 (Main Text), we can consider the empirical-Bayes hierarchical model as a mixed model with both fixed and random effects in the frequentist setting as given by: logit(pmi) = αm + ZmγXmi +Xmiτ tmm, where ZmγXmi would be considered a fixed effect and Xmiτ tmm a random effect. Alternatively, this model may be viewed in the Bayesian framework, where the posterior distribution for the unknown parameters given the data is given by:
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Exploration of empirical Bayes hierarchical modeling for the analysis of genome-wide association study data.
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