lr_qvb_global

A variational Bayesian algorithm is proposed for multi-source heterogeneous quantile models under the
Spike-and-Slab prior, enabling simultaneous variable selection for both homogeneous and
heterogeneous covariates.

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.

Lu Luo

LRQVB

Low Rank Correction Quantile Variational Bayesian Algorithm for
Multi-Source Heterogeneous Models

Huiqiong Li

lr_qvb_global function

<dl><dt>X</dt>
<dd>Homogeneous covariates</dd>
<dt>Z</dt>
<dd>Heterogeneous covariates</dd>
<dt>y</dt>
<dd>Response covariates</dd>
<dt>tau</dt>
<dd>Quantile Levels</dd>
<dt>eps</dt>
<dd>Algorithm convergence tolerance, Defaut:1e-3</dd></dl>

Arguments

Global Low Rank Correction Quantile VB — lr_qvb_global

<dl>

<dt>X</dt>
<dd>Homogeneous covariates</dd>


<dt>Z</dt>
<dd>Heterogeneous covariates</dd>


<dt>y</dt>
<dd>Response covariates</dd>


<dt>tau</dt>
<dd>Quantile Levels</dd>


<dt>eps</dt>
<dd>Algorithm convergence tolerance, Defaut:1e-3</dd>

</dl>

lr_qvb_global: Global Low Rank Correction Quantile VB

Description

Usage

Value

Arguments