Browse Items (8208 total)

Recent advances in computational methods for intractable models have made network
data increasingly amenable to statistical analysis. Exponential random graph models
(ERGMs) emerged as one of the main families of models capable of capturing the…

The popularity of Bayesian statistical methods has increased dramatically in recent
years across many research areas and industrial applications. This is the result of a variety
of methodological advances with faster and cheaper hardware as well as…

High frequency data typically exhibit asynchronous trading and microstructure noise,
which can bias the covariances estimated by standard estimators. While a number of
specialized estimators have been proposed, they have had limited availability in…

Mediation analysis is one of the most widely used statistical techniques in the social,
behavioral, and medical sciences. Mediation models allow to study how an independent
variable affects a dependent variable indirectly through one or more…

Consider a Bayesian inference problem where a variable of interest does not take
values in a Euclidean space. These “non-standard” data structures are in reality fairly
common. They are frequently used in problems involving latent discrete factor…

This paper introduces the R package exuber for testing and date-stamping periods of
mildly explosive dynamics (exuberance) in time series. The package computes test statistics for the supremum augmented Dickey-Fuller test (SADF) of Phillips, Wu, and…

UComp is a powerful library for building unobserved components models, useful for
forecasting and other important operations, such us de-trending, cycle analysis, seasonal
adjustment, signal extraction, etc. One of the most outstanding features…

Reproducible document standards, like R Markdown, facilitate the programmatic creation of documents whose content is itself programmatically generated. While programmatic content alone may not be sufficient for a rendered document since it does…

This article presents a new implementation of hierarchical clustering for the R language
that allows one to apply spatial or temporal contiguity constraints during the clustering
process. The need for contiguity constraint arises, for instance,…

Actuaries model insurance claim amounts using heavy tailed probability distributions.
They routinely need to evaluate quantities related to these distributions such as quantiles
in the far right tail, moments or limited moments. Furthermore,…