Estimation of distribution algorithms based on copula, and a number of different distribution functions were selected as the dimension of the marginal distribution function by applying K-S test. Dynamic K-S test, that is, dynamic adjust frequency of K-S test in different stages. In the estimation of the probability model stage, according to the actual distribution of the dominant group respect...

A Skewed Student-t Realised DCC copula model using Realised Volatility GARCH marginal functions is developed within a Bayesian framework for the purpose of forecasting portfolio Value at Risk and Conditional Value at Risk. The use of copulas is implemented so that the marginal distributions can be separated from the dependence structure to produce tail forecasts. This is compared to using tradi...

This paper studies the estimation of a class of copula-based semiparametric stationary Markov models. These models are characterized by nonparametric marginal distributions and parametric copula functions, while the copulas capture all the scale-free temporal dependence of the processes. Simple estimators of the marginal distribution and the copula parameter are provided, and their asymptotic p...

This article investigates the relationship between daily crude oil prices and exchange rates. Functional data analysis is used to show the clustering pattern of exchange rates and oil prices over the time period through high dimensional visualizations. We select exchange rates for important currencies related to crude oil prices by using the objective Bayesian variable selection method. The sel...

Parametric conditional copulamodels allow the copula parameters to vary with a set of covariates according to an unknown calibration function. Flexible Bayesian inference for the calibration function of a bivariate conditional copula is introduced. The prior distribution over the set of smooth calibration functions is built using a sparse Gaussian process (GP) prior for the single index model (...

Copulae are joint continuous distributions with uniform marginals and have been proposed to capture probabilistic dependence between random variables. Maximum entropy copulae introduced by Bedford and Meeuwissen (1997) provide experts the option of making minimally informative assumptions given a degree of dependence constraint between two random variables. Unfortunately, their distributions fu...

The literature has earlier examples that pointed out the weaknesses in traditional Monte Carlo simulations in modelling electricity prices. For example, Lucia and Schwartz (2002) applied EVT to electricity prices to assess the level of tail fatness in series followed by comparison of the Monte Carlo simulation results based on Gaussian and student-t distributions. Their simulation results impro...

With clustered event time data, interest most often lies in marginal features such as quantiles or probabilities from the marginal event time distribution or covariate effects on marginal hazard functions. Copula models offer a convenient framework for modeling. We present methods of estimating the baseline marginal distributions, covariate effects, and association parameters for clustered curr...

We discuss a class of copula-based ordered probit models with endogenous switching. Such models can be useful for the analysis of self-selection in subjective well-being equations in general, and job satisfaction in particular, where assignment of regressors may be endogenous rather than random, resulting from individual maximization of well-being. In an application to public and private sector...

in the literature of life-testing, general progressive censoring has been studied extensively. but, all the results have been developed under the key assumption that the units undertest are independently distributed. in this paper, we study general progressively type-ii censored order statistics arising from identical units under test which are jointly distributed according to an archimedean co...