In statistics, time-to-event analysis methods traditionally focus on the estimation of hazards. recent years, machine learning have been proposed to directly predict event times. A method based vine copula models is make point and interval predictions for a right-censored response variable given mixed discrete-continuous explanatory variables. Extensive experiments simulated real datasets show ...

We propose the ‘Copula PC’ algorithm for causal discovery from a combination of continuous and discrete data, assumed to be drawn from a Gaussian copula model. It is based on a two-step approach. The first step applies Gibbs sampling on rank-based data to obtain samples of correlation matrices. These are then translated into an average correlation matrix and an effective number of data points, ...

Most existing copula models for dependent censoring in the literature assume that parameter defining is known. However, prior knowledge on this dependence often unavailable. In article we propose a novel model under which does not need to be The based parametric relation between survival time (T) and (C), whereas marginal distributions of T C follow semiparametric Cox proportional hazards model...

This paper used different copula-based GARCH models (Copula-GARCH model and Copula-GJR-GARCH model) to analyze the dependence structure among gold price, stock price index of gold mining companies and Shanghai Composite Index in China. The empirical results found that the suitable margins were skew-t distribution, and the GJR-GARCH marginal distribution had better explanatory ability than the G...

In order to capture the dependency among exchange rates we construct semiparametric multivariate copula models with ARMA-GARCH margins. As multivariate copula models we utilize pair-copula constructions (PCC) such as regular and canonical vines. As building blocks of the PCC’s we use bivariate t-copulas for different tail dependence between pairs of exchange rates. Alternatively we also conside...

In this research we introduce a new class of multivariate probability models to the marketing literature. Known as “copula models”, they have a number of attractive features. First, they permit the combination of any univariate marginal distributions that need not come from the same distributional family. Second, a particular class of copula models, called “elliptical copula”, have the property...