52 citations · 72 across the 11 of their papers we have counts for
11 papers
Vine dependence graphs with latent variables as summaries for gene expression data
Xinyao Fan, Harry Joe, Yongjin Park
The advent of high-throughput sequencing technologies has lead to vast comparative genome sequences. The construction of gene-gene interaction networks or dependence graphs on the…
Margin-closed vector autoregressive time series models
Lin Zhang, Harry Joe, Natalia Nolde
Conditions are obtained for a Gaussian vector autoregressive time series of order , VAR(), to have univariate margins that are autoregressive of order or lower-dimensiona…
High-dimensional factor copula models with estimation of latent variables
Xinyao Fan, Harry Joe
Factor models are a parsimonious way to explain the dependence of variables using several latent variables. In Gaussian 1-factor and structural factor models (such as bi-factor, ob…
Predicting Times to Event Based on Vine Copula Models
Shenyi Pan, Harry Joe
In statistics, time-to-event analysis methods traditionally focus on the estimation of hazards. In recent years, machine learning methods have been proposed to directly predict the…
On the Selection of Loss Severity Distributions to Model Operational Risk
Daniel Hadley, Harry Joe, Natalia Nolde
Accurate modeling of operational risk is important for a bank and the finance industry as a whole to prepare for potentially catastrophic losses. One approach to modeling operation…
Conditional Inferences Based on Vine Copulas with Applications to Credit Spread Data of Corporate Bonds
Shenyi Pan, Harry Joe, Guofu Li
Understanding the dependence relationship of credit spreads of corporate bonds is important for risk management. Vine copula models with tail dependence are used to analyze a credi…