27 citations · 59 across the 11 of their papers we have counts for
10 papers · 1 filter
Dynamic models using score copula innovations
Landan Zhang, Michael K. Pitt, Robert Kohn
This paper introduces a new class of observation driven dynamic models. The time evolving parameters are driven by innovations of copula form. The resulting models can be made stri…
Bayesian inference for latent factor GARCH models
Michael K. Pitt, Jamie Hall, Robert Kohn
Latent factor GARCH models are difficult to estimate using Bayesian methods because standard Markov chain Monte Carlo samplers produce slowly mixing and inefficient draws from the…
Annealed Important Sampling for Models with Latent Variables
M. -N. Tran, C. Strickland, M. K. Pitt +1
This paper is concerned with Bayesian inference when the likelihood is analytically intractable but can be unbiasedly estimated. We propose an annealed importance sampling procedur…
Importance sampling squared for Bayesian inference in latent variable models
Minh-Ngoc Tran, Marcel Scharth, Michael K. Pitt +1
We consider Bayesian inference by importance sampling when the likelihood is analytically intractable but can be unbiasedly estimated. We refer to this procedure as importance samp…
On the existence of moments for high dimensional importance sampling
Michael K. Pitt, Minh-Ngoc Tran, Marcel Scharth +1
Theoretical results for importance sampling rely on the existence of certain moments of the importance weights, which are the ratios between the proposal and target densities. In p…
Copula-type Estimators for Flexible Multivariate Density Modeling using Mixtures
Minh-Ngoc Tran, Paolo Giordani, Xiuyan Mun +2
Copulas are popular as models for multivariate dependence because they allow the marginal densities and the joint dependence to be modeled separately. However, they usually require…