In this paper, the one-way ANOVA model and its application in Bayesian multi-class variable selection is considered. A full Bayesian bootstrap prior ANOVA test function is developed within the framework of parametric empirical Bayes. The test function developed was later used for variable screening in multiclass classification scenario. Performance comparison between the proposed method and existing classical ANOVA method was achieved using simulated and real life gene expression datasets. Analysis results revealed lower false positive rate and higher sensitivity for the proposed method.
A new Bayesian estimation procedure for extended cox model with time varying covariate was presented. The prior was determined using bootstrapping technique within the framework of parametric empirical Bayes. The efficiency of the proposed method was observed using Monte Carlo simulation of extended Cox model with time varying covariates under varying scenarios. Validity of the proposed method was also ascertained using real life data set of Stanford heart transplant. Comparison of the proposed method with its competitor established appreciable supremacy of the method.
In this study, the Variational Bayes (VB) approach was hybridized with the bootstrap prior procedure to improve the accuracy of subset selection as well as optimizing the algorithm time in modelling high-dimensional genomic data with inherent sparse structure. The new hybrid VB approach is shown to yields a minimal sufficient statistic which under mild regularity conditions converges to the true sparse structure. Simulation and real-life high-dimensional genomic data experiments revealed comparable empirical performance with other competing frequentist and Bayesian methods. In addition, a new fast algorithm that illustrates the procedure was developed and implemented in the environment of R statistical software as package “VBbootprior”.
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