Efficient Bayesian inference for Gaussian copula regression models

By Michael K. Pitt, David Chan, Robert Kohn. 2006

289 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/32fc07d1a5bdb7a30d510aeecfed7d3c982f7415

How researchers see this paper

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

In Artificial Intelligence

In Finance

In Statistics and Probability

Citations in the corpus

Modelling dependence using skew t copulas: Bayesian inference and applications (2012, unknown): “Sample from f(Γ∣x, {φ∖Γ},Θ, z) using the method of Pitt et al. (2006) or alternative.”
Bayesian inference for multivariate copulas using pair-copula constructions. (2010, methodology): “Pitt et al. (2006) deal with Gaussian copula regression.”
Copula-Based Models for Multivariate Discrete Response Data (2013, methodology): “Smith, Gan and Kohn [53] extend the work in [49] to other elliptical copula-based models.”
Multivariate non-normally distributed random variables in climate research - introduction to the copula approach (2008, background): “Further information is given in e.g. Em-brechts et al. (2001); Pitt et al. (2006); Renard and Lang (2007); Clemen and Reilly (1999).”
Multivariate stochastic volatility (2009, background): “Another type of factor MSV model is considered in Pitt and Shephard (1999), who, following a model proposed in Kim et al.”
Multilevel models with multivariate mixed response types (2009, methodology): “Instead, we can use multiple imputation ( Rubin, 1987, Kenward and Carpenter, 2007),”
Modeling Longitudinal Data Using a Pair-Copula Decomposition of Serial Dependence (2010, unknown): “However, because the likelihood can be computed in closed form, the copula parameters can be estimated joint with any marginal parameters by appending additional MH steps as outlined by Pitt et al. (2006) for Gaussian copula.”
A Semiparametric Gaussian Copula Regression Model for Predicting Financial Risks from Earnings Calls (2014, background): “…to calculate the expected value of the CDF of the label: where Unfortunately, the exact inference can be intractable in the multivariate case, and approximate inference, such as Markov Chain Monte Carlo sampling (Gelfand and Smith, 1990; Pitt et al., 2006) is often used for posterior inference.”
MCMC Estimation of Restricted Covariance Matrices (2009, methodology): “One-at-a-time MH sampling of the components of a correlation matrix has been implemented in Gaussian copula models by Pitt, Chan, and Kohn (2006) using the parameterization of Wong, Carter, and Kohn (2003), and in hierarchical shrinkage models by Barnard, McCulloch, and Meng (2000) using the griddy…”