LRQVB - Low Rank Correction Quantile Variational Bayesian Algorithm for
Multi-Source Heterogeneous Models
A Low Rank Correction Variational Bayesian algorithm for
high-dimensional multi-source heterogeneous quantile linear
models. More details have been written up in a paper submitted
to the journal Statistics in Medicine, and the details of
variational Bayesian methods can be found in Ray and Szabo
(2021) <doi:10.1080/01621459.2020.1847121>. It simultaneously
performs parameter estimation and variable selection. The
algorithm supports two model settings: (1) local models, where
variable selection is only applied to homogeneous coefficients,
and (2) global models, where variable selection is also
performed on heterogeneous coefficients. Two forms of parameter
estimation are output: one is the standard variational Bayesian
estimation, and the other is the variational Bayesian
estimation corrected with low-rank adjustment.
