dsp_fit: MCMC Sampler for Models with Dynamic Shrinkage Processes

In dsp: Dynamic Shrinkage Process and Change Point Detection

MCMC Sampler for Models with Dynamic Shrinkage Processes

Description

Wrapper function for fitting models with Dynamic Shrinkage Processes (DSP), including:

• Adaptive Bayesian Changepoint analysis and local Outlier (ABCO),

• Bayesian Trend Filter for Gaussian Data

• Time-varying Regression

• Bayesian Trend Filter with B-spline for irregularly spaced or functional time-series.

• Bayesian Smoothing for Count Data

Method for printing basic information about the MCMC sampling settings for the fitted model

Usage

dsp_fit(
  y,
  model_spec,
  nsave = 1000,
  nburn = 1000,
  nskip = 4,
  computeDIC = TRUE,
  verbose = TRUE,
  ...
)

## S3 method for class 'dsp'
print(x, ...)

Arguments

y	a numeric vector of the T x 1 vector of time series observations
model_spec	a list containing model specification generated from dsp_spec().
nsave	integer scalar (default = 1000); number of MCMC iterations to record
nburn	integer scalar (default = 1000); number of MCMC iterations to discard (burn-in)
nskip	integer scalar (default = 4); number of MCMC iterations to skip between saving iterations, i.e., save every (nskip + 1)th draw
computeDIC	logical; if TRUE (default), compute the deviance information criterion DIC and the effective number of parameters p_d
verbose	logical; should extra information on progress be printed to the console? Defaults to FALSE
...	currently not used
x	object of class dsp from dsp_fit()

Details

A brief summary of the settings used to fit the model including number of iterations, burn in, and thinning rates.

Value

dsp_fit returns an object of class "dsp".

An object of class "dsp" is defined as a list containing at least the following components:

mcmc_output	a list of the nsave MCMC samples for the parameters named in mcmc_params
DIC	Deviance Information Criterion
mcpar	named vector of supplied nsave, nburn, and nskip
model_spec	the object supplied for model_spec argument

MCMC Output Parameters

Shared parameters across wrappers include:

• mu: Conditional mean.

• ypred: Posterior predictive \tilde{y}_t.

• evol_sigma_t2: Variance of the state variable.

• obs_sigma_t2: Observation variance \sigma^2_{\epsilon} (family = "gaussian").

• dhs_phi, dhs_mean: DHS hyperparameter draws (evol_error = "DHS").

Model-specific parameters include:

• changepoint (Gaussian):

  • omega: D-th differenced posterior mean draws, \mu_t.

  • zeta: anomaly component (when useAnom = TRUE).

  • zeta_sigma_t2: anomaly variance (when useAnom = TRUE).

  • gamma: threshold parameter in the thresholded DHS log-volatility dynamics.

• regression (Gaussian):

  • beta: time-varying regression coefficient draws.

• bspline (Gaussian):

  • beta: B-spline basis coefficient draws.

• smoothing (Negative Binomial):

  • r: overdispersion parameter draws.

Note

The data y may contain NAs, which will be treated with a simple imputation scheme via an additional Gibbs sampling step. In general, rescaling y to have unit standard deviation is recommended to avoid numerical issues when family is "gaussian".

Examples

set.seed(200)
signal = c(rep(0, 50), rep(10, 50))
noise = rep(1, 100)
noise_var = rep(1, 100)
for (k in 2:100){
  noise_var[k] = exp(0.9*log(noise_var[k-1]) + rnorm(1, 0, 0.5))
  noise[k] = rnorm(1, 0, sqrt(noise_var[k])) }

y = signal + noise
model_spec = dsp_spec(family = "gaussian", model = "changepoint",
                      D = 1, useAnom = TRUE, obsSV = "SV")
mcmc_output = dsp_fit(y, model_spec = model_spec, nsave = 500, nburn = 500)

print(mcmc_output)

dsp documentation built on July 17, 2026, 1:06 a.m.