14 citations · 35 across the 35 of their papers we have counts for
17 papers · 1 filter
Bayesian copula-based spatial random effects models for inference with complex spatial data
Alan Pearse, David Gunawan, Noel Cressie
In this article, we develop fully Bayesian, copula-based, spatial-statistical models for large, noisy, incomplete, and non-Gaussian spatial data. Our approach includes novel constr…
Bayesian Inference for Non-Gaussian Simultaneous Autoregressive Models with Missing Data
Anjana Wijayawardhana, David Gunawan, Thomas Suesse
Standard simultaneous autoregressive (SAR) models typically assume normally distributed errors, an assumption often violated in real-world datasets that frequently exhibit non-norm…
Fast Variational Boosting for Latent Variable Models
David Gunawan, David Nott, Robert Kohn
We consider the problem of estimating complex statistical latent variable models using variational Bayes methods. These methods are used when exact posterior inference is either in…
Variational Bayes Inference for Spatial Error Models with Missing Data
Anjana Wijayawardhana, David Gunawan, Thomas Suesse
The spatial error model (SEM) is a type of simultaneous autoregressive (SAR) model for analysing spatially correlated data. Markov chain Monte Carlo (MCMC) is one of the most widel…
A Marginal Maximum Likelihood Approach for Hierarchical Simultaneous Autoregressive Models with Missing Data
Anjana Wijayawardhana, Thomas Suesse, David Gunawan
Efficient estimation methods for simultaneous autoregressive (SAR) models with missing data in the response variable have been well-explored in the literature. A common practice is…
Optimal prediction of positive-valued spatial processes: asymmetric power-divergence loss
Alan R. Pearse, Noel Cressie, David Gunawan
This article studies the use of asymmetric loss functions for the optimal prediction of positive-valued spatial processes. We focus on the family of power-divergence loss functions…
Reliable Bayesian Inference in Misspecified Models
David T. Frazier, Robert Kohn, Christopher Drovandi +1
We provide a general solution to a fundamental open problem in Bayesian inference, namely poor uncertainty quantification, from a frequency standpoint, of Bayesian methods in missp…
Bayesian Inference for Evidence Accumulation Models with Regressors
Viet Hung Dao, David Gunawan, Robert Kohn +3
Evidence accumulation models (EAMs) are an important class of cognitive models used to analyze both response time and response choice data recorded from decision-making tasks. Deve…
New Estimation Approaches for the Hierarchical Linear Ballistic Accumulator Model
David Gunawan, Guy E. Hawkins, Minh-Ngoc Tran +2
The Linear Ballistic Accumulator (Brown & Heathcote, 2008) model is used as a measurement tool to answer questions about applied psychology. The analyses based on this model depend…
Robust Particle Density Tempering for State Space Models
David Gunawan, Robert Kohn, Minh Ngoc Tran
Density tempering (also called density annealing) is a sequential Monte Carlo approach to Bayesian inference for general state models; it is an alternative to Markov chain Monte Ca…
The Correlated Particle Hybrid Sampler for State Space Models
David Gunawan, Chris Carter, Robert Kohn
Particle Markov Chain Monte Carlo (PMCMC) is a general computational approach to Bayesian inference for general state space models. Our article scales up PMCMC in terms of the numb…
Efficient Bayesian inference for multivariate factor stochastic volatility models with leverage
David Gunawan, Chris Carter, Robert Kohn
This paper discusses the efficient Bayesian estimation of a multivariate factor stochastic volatility (Factor MSV) model with leverage. We propose a novel approach to construct the…
Efficient Bayesian estimation for flexible panel models for multivariate outcomes: Impact of life events on mental health and excessive alcohol consumption
David Gunawan, Chris carter, Denzil Fiebig +1
The problem we consider considers estimating a multivariate longitudinal panel data model whose outcomes can be a combination of discrete and continuous variables. This problem is…
Fast Inference for Intractable Likelihood Problems using Variational Bayes
David Gunawan, Minh-Ngoc Tran, Robert Kohn
Variational Bayes (VB) is a popular estimation method for Bayesian inference. However, most existing VB algorithms are restricted to cases where the likelihood is tractable, which…
Bayesian Inference for State Space Models using Block and Correlated Pseudo Marginal Methods
P. Choppala, D. Gunawan, J. Chen +2
This article addresses the problem of efficient Bayesian inference in dynamic systems using particle methods and makes a number of contributions. First, we develop a correlated pse…
Computationally Efficient Bayesian Estimation of High Dimensional Copulas with Discrete and Mixed Margins
D. Gunawan, M. -N. Tran, K. Suzuki +2
Estimating copulas with discrete marginal distributions is challenging, especially in high dimensions, because computing the likelihood contribution of each observation requires ev…
Mixed Marginal Copula Modeling
David Gunawan, Mohamad A. Khaled, Robert Kohn
This article extends the literature on copulas with discrete or continuous marginals to the case where some of the marginals are a mixture of discrete and continuous components. We…