1 citations · 2 across the 10 of their papers we have counts for
8 papers · 1 filter
Restricted Search Space Graph MCMC via Birth-Death Processes
Morris Greenberg, Kieran R Campbell, Radu Craiu
Inferring directed acyclic graphs (DAGs) from data via Markov chain Monte Carlo (MCMC) is computationally challenging in moderate-to-high dimensional settings because their discret…
Likelihood-based inference for the Gompertz model with Poisson errors
Paolo Onorati, Sofia Ruiz-Suarez, Radu Craiu
Population dynamics models play an important role in a number of fields, such as actuarial science, demography, and ecology, as they help explain past fluctuations and predict futu…
Compressed Bayesian Tensor Regression
Roberto Casarin, Radu Craiu, Qing Wang
To address the common problem of high dimensionality in tensor regressions, we introduce a generalized tensor random projection method that embeds high-dimensional tensor-valued co…
Bayesian nonparametric mixtures of Archimedean copulas
Ruyi Pan, Luis E. Nieto-Barajas, Radu V. Craiu
Copula-based dependence modeling often relies on parametric formulations. This is mathematically convenient, but can be statistically inefficient when the parametric families are n…
Markov Switching Multiple-equation Tensor Regressions
Roberto Casarin, Radu Craiu, Qing Wang
We propose a new flexible tensor model for multiple-equation regression that accounts for latent regime changes. The model allows for dynamic coefficients and multi-dimensional cov…
Multivariate temporal dependence via mixtures of rotated copulas
Ruyi Pan, Luis E. Nieto-Barajas, Radu Craiu
Parametric copula families have been known to flexibly capture various dependence patterns, e.g., either positive or negative dependence in either the lower or upper tails of bivar…