Open AccessSubset Selection in High-Dimensional Genomic Data using Hybrid Variational Bayes and Bootstrap priors
Author(s) -
Oyebayo Ridwan Olaniran,
Mohd Asrul Affendi Abdullah
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1489/1/012030
Subject(s) - frequentist inference , bayes' theorem , prior probability , computer science , statistic , bayesian probability , selection (genetic algorithm) , bayes factor , algorithm , mathematics , data mining , bayesian inference , machine learning , artificial intelligence , statistics
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”.