Multivariate Dispersion Models Generated From Gaussian Copula

By Peter X.-K. Song. 2000

393 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1c91bf49a1162c392d1c427d08489e67675f0ab8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

Copula Modeling: An Introduction for Practitioners (2007, methodology): “4), one can use the maximization-by-parts (MBP) algorithm recently suggested by Song et al. (2005). This involves starting from an initial estimate of (β1,β2) based on maximization of L1,N (β1,β2), and then using Eq.”
Multivariate Frequency-Severity Regression Models in Insurance (2016, methodology): “Copula Regression Copulas have been applied with GLMs in the biomedical literature since the mid-1990s ([16–18]).”
Multivariate Survival Modelling: A Unified Approach with Copulas (2001, background): “Let us now consider the LMP class: S (x1 + t1, x2 + t2) = S (x1, x2)S (t1, t2) for all x1, x2 ≥ 0 and t1, t2 ≥ 0 (140) Marshall and Olkin [1967] show that the solution of this functional equation is the product of two exponential survival functions.”
Regularized rank-based estimation of high-dimensional nonparanormal graphical models (2012, background): “The semiparametric Gaussian copula model is a nice combination of ﬂexibility and interpretability, and it has generated a lot of interests in statistics, econometrics and ﬁnance; see Song (2000), Chen and Fan (2006), Chen, Fan and Tsyrennikov (2006) and references therein.”
Regression in a copula model for bivariate count data (2010, methodology): “[4] used the Frank copula to model bivariate count data, while Van Ophem [30], Song [29], and Lee [20] used a normal copula-based model.”
Non‐parametric Estimation of Tail Dependence (2006, background): “Copulae itself have become quite prominent in theory and applications (see e.g. Sklar, 1996; Song, 2000; Cuculescu & Theodorescu, 2003; Embrechts et al., 2003).”
Total loss estimation using copula-based regression models (2012, methodology): “r the two marginal regression models with copula families. This is an extension of a recent approach by Czado et al. (2011) and De Leon and Wu (2011) who only consider a Gauss copula based on work by Song (2000, 2007) and Song et al. (2009). In our general copula-based regression approach, the model”
Bayesian model selection for D‐vine pair‐copula constructions (2011, background): “If the df parameter ν of a bivariate t−copula c(·, ·|ρ, ν) converges to infinity then a Gaussian copula (see Song, 2000) with parameter ρ is obtained in the limit.”
Modelling dependence using skew t copulas: Bayesian inference and applications (2012, neutral): “In this situation copulas constructed from underlying elliptical distributions are popular, such as the Gaussian (Song 2000) or t (Demarta & McNeil 2005) copulas.”
Uncertainty analysis of bias from satellite rainfall estimates using copula method (2014, background): “Several studies also have highlighted the application of Gaussian copula to describe the dependence between variables in multivariate distributions (Song, 2000; Renard and Lang, 2007; Madsen, 2009; Song et al., 2009; Aghakouchak et al., 2010a,b,c; Fang, 2012).”
Statistical reconstruction of two-phase random media (2014, neutral): “The nonlinear transformation of Gaussian fields has been extensively used in modeling multivariate distributions [21,22].”