generate_ly2hat

Posterior predictive sampler on the transformed y (log(y^2))

Provides efficient Markov chain Monte Carlo (MCMC) algorithms for
dynamic shrinkage processes, which extend global-local shrinkage priors to
the time series setting by allowing shrinkage to depend on its own past.
These priors yield locally adaptive estimates, useful for time series and
regression functions with irregular features. The package includes full MCMC
implementations for trend filtering using dynamic shrinkage on signal differences,
producing locally constant or linear fits with adaptive credible bands.
Also included are models with static shrinkage and normal-inverse-Gamma priors for comparison.
Additional tools cover dynamic regression with time-varying coefficients and
B-spline models with shrinkage on basis differences, allowing for flexible
curve-fitting with unequally spaced data. Some support for heteroscedastic errors,
outlier detection, and change point estimation.
Methods in this package are described in Kowal et al. (2019) <doi:10.1111/rssb.12325>,
Wu et al. (2024) <doi:10.1080/07350015.2024.2362269>, Schafer and Matteson (2024)
<doi:10.1080/00401706.2024.2407316>, and Cho and Matteson (2024) <doi:10.48550/arXiv.2408.11315>.

Toryn Schafer

Dynamic Shrinkage Process and Change Point Detection

Daniel R. Kowal

Haoxuan Wu

Jason B. Cho

David S. Matteson

generate_ly2hat function

<dl><dt>h</dt>
<dd>the log varaince term h</dd>
<dt>p_error_term</dt>
<dd>2 dimensional data frame containing mean and the variance from the 10 componenet
Gaussian mixture in Omori et al 2007 paper.</dd></dl>

Arguments

Posterior predictive sampler on the transformed y (log(y^2)) — generate_ly2hat

<dl>

<dt>h</dt>
<dd>the log varaince term h</dd>


<dt>p_error_term</dt>
<dd>2 dimensional data frame containing mean and the variance from the 10 componenet
Gaussian mixture in Omori et al 2007 paper.</dd>

</dl>

generate_ly2hat: Posterior predictive sampler on the transformed y (log(y^2))

Description

Usage

Value

Arguments