sampleBTF_sparse: Sampler for first or second order random walk (RW) Gaussian...

In dsp: Dynamic Shrinkage Process and Change Point Detection

Sampler for first or second order random walk (RW) Gaussian dynamic linear model (DLM) with additional shrinkage to zero

Description

Compute one draw of the T x 1 state variable mu in a DLM using back-band substitution methods. This model is equivalent to the Bayesian trend filtering (BTF) model, assuming appropriate (shrinkage/sparsity) priors for the evolution errors, with an additional shrinkage-to-zero prior.

Usage

sampleBTF_sparse(
  y,
  obs_sigma_t2,
  evol_sigma_t2,
  zero_sigma_t2,
  D = 1,
  chol0 = NULL
)

Arguments

y	the T x 1 vector of time series observations
obs_sigma_t2	the T x 1 vector of observation error variances
evol_sigma_t2	the T x 1 vector of evolution error variances
zero_sigma_t2	the T x 1 vector of shrink-to-zero variances
D	the degree of differencing (one or two)
chol0	(optional) the m x m matrix of initial Cholesky factorization; if NULL, use the Matrix package for sampling, otherwise use the spam package

Value

T x 1 vector of simulated states

Note

Missing entries (NAs) are not permitted in y. Imputation schemes are available.

dsp documentation built on Aug. 21, 2025, 5:29 p.m.