Browse Items (20 total)

In this summary we introduce the papers published in the special issue on Bayesian
statistics. This special issue comprises 20 papers on Bayesian statistics and Bayesian
inference on different topics such as general packages for hierarchical linear…

Time-varying parameter (TVP) models are widely used in time series analysis to flexibly deal with processes which gradually change over time. However, the risk of overfitting
in TVP models is well known. This issue can be dealt with using…

nimble is an R package for constructing algorithms and conducting inference on hierarchical models. The nimble package provides a unique combination of flexible model
specification and the ability to program model-generic algorithms. Specifically,…

Generalized additive models (GAMs) are flexible non-linear regression models, which
can be fitted efficiently using the approximate Bayesian methods provided by the mgcv R
package. While the GAM methods provided by mgcv are based on the assumption…

In this study, we present a new module built for users interested in a programming
language similar to BUGS to fit a Bayesian model based on the piecewise exponential (PE)
distribution. The module is an extension to the open-source program JAGS by…

Missing data occur in many types of studies and typically complicate the analysis.
Multiple imputation, either using joint modeling or the more flexible fully conditional
specification approach, are popular and work well in standard settings. In…

The INLA package provides a tool for computationally efficient Bayesian modeling
and inference for various widely used models, more formally the class of latent Gaussian
models. It is a non-sampling based framework which provides approximate…

Stochastic volatility (SV) models are nonlinear state-space models that enjoy increasing popularity for fitting and predicting heteroskedastic time series. However, due to the
large number of latent quantities, their efficient estimation is…

Booming in business and a staple analysis in medical trials, the A/B test assesses
the effect of an intervention or treatment by comparing its success rate with that of a
control condition. Across many practical applications, it is desirable that…

Structural equation models comprise a large class of popular statistical models, including factor analysis models, certain mixed models, and extensions thereof. Model
estimation is complicated by the fact that we typically have multiple…