Statistical Models Commons^™

We propose a new variational Bayes estimator for high-dimensional copulas with discrete, or a combination of discrete and continuous, margins. The method is based on a variational approximation to a tractable augmented posterior, and is faster than previous likelihood-based approaches. We use it to estimate drawable vine copulas for univariate and multivariate Markov ordinal and mixed time series. These have dimension $rT$, where $T$ is the number of observations and $r$ is the number of series, and are difficult to estimate using previous methods. 

The vine pair-copulas are carefully selected to allow for heteroskedasticity, which is a feature of most ordinal …

Motivated by the evaluation of the causal effect of the General Agreement on Tariffs and Trade on bilateral international trade flows, we investigate the role of network structure in propensity score matching under the assumption of strong ignorability. We study the sensitivity of causal inference with respect to the presence of characteristics of the network in the set of confounders conditional on which strong ignorability is assumed to hold. We find that estimates of the average causal effect are highly sensitive to the presence of node-level network statistics in the set of confounders. Therefore, we argue that estimates may suffer …

The Valente Collection is a 29,000 volume special collection that bridges Italian and Italian American history, literature, religion and art. It is a unique asset for the library and the university. One concept for promoting this collection and offering insight into the holdings is visualization. This goal of this poster is to help academic librarians assess which tools are most appropriate to create visualizations of current collections. Examples of different visualization types are explained including Excel Power Map. Tableau and Datawrapper.

A number of classical approaches to nonparametric regression have recently been extended to the case of functional predictors. This paper introduces a new method of this type, which extends intermediate-rank penalized smoothing to scalar-on-function regression. The core idea is to regress the response on leading principal coordinates defined by a relevant distance among the functional predictors, while applying a ridge penalty. Our publicly available implementation, based on generalized additive modeling software, allows for fast optimal tuning parameter selection and for extensions to multiple functional predictors, exponential family-valued responses, and mixed-effects models. In an application to signature verification data, the proposed …