set_horseshoe: Horseshoe Prior Specification

In bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling

Horseshoe Prior Specification

Description

Set initial hyperparameters and parameter before starting Gibbs sampler for Horseshoe prior.

Usage

set_horseshoe(local_sparsity = 1, group_sparsity = 1, global_sparsity = 1)

## S3 method for class 'horseshoespec'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

is.horseshoespec(x)

## S3 method for class 'horseshoespec'
knit_print(x, ...)

Arguments

local_sparsity	Initial local shrinkage hyperparameters
group_sparsity	Initial group shrinkage hyperparameters
global_sparsity	Initial global shrinkage hyperparameter
x	Any object
digits	digit option to print
...	not used

Details

Set horseshoe prior initialization for VAR family.

• local_sparsity: Initial local shrinkage

• group_sparsity: Initial group shrinkage

• global_sparsity: Initial global shrinkage

In this package, horseshoe prior model is estimated by Gibbs sampling, initial means initial values for that gibbs sampler.

References

Carvalho, C. M., Polson, N. G., & Scott, J. G. (2010). The horseshoe estimator for sparse signals. Biometrika, 97(2), 465-480.

Makalic, E., & Schmidt, D. F. (2016). A Simple Sampler for the Horseshoe Estimator. IEEE Signal Processing Letters, 23(1), 179-182.

bvhar documentation built on April 4, 2025, 5:22 a.m.