I am currently a postdoctoral fellow in the school of Economics, UNSW business School, University of New South Wales, Australia. I received my PhD in 2016 in Econometrics from Monash University, Australia. My main research interests lie in Bayesian methodology, in particular Markov chain Monte Carlo, fast Variational Bayes, sequential Monte Carlo, Importance Sampling, Particle Filtering, Intractable Likelihood Problems.
I am also interested in Big Data, copula, mixture models, Hamiltonian Monte Carlo, and inference for income distribution, mobility, inequality, and poverty.
A copula is a multivariate model whose marginals are uniform. A copula based model is one that is based on a copula. The attraction of using copula models is that we can separately model the marginal distributions and the dependence structure of our target distribution. Such models are particularly attractive when some or all of the marginals are discrete or a mixture of discrete and continuous components. A multivariate probit model is one simple example of a copula model.
Many statistical applications use models that incorporate latent variables. For example, random effect panel data models, use latent variables to account for dependence between observations. State space models whose latent variables follow a Markov process, are used in economics, finance, and engineering.
This project pursues breakthroughs which allow important questions of basic and applied science to be addressed using mathematical decision models. Advances are made through an interdisciplinary effort, combining recent developments in econometric and statistical methods, cognitive science, and advances in computing. The outcomes will bring to a new range of questions a proven and powerful approach for investigating psychological effects. The project will begin this expansion effort with investigations of choices about health care and consumer purchases.
Invited talks, refereed proceedings and other conference outputs
Gunawan, D., Carter C., Fiebig D. G., & Kohn R.
(2017). Efficient Bayesian Estimation for Flexible Panel Models for Multivariate Outcomes: Impact of Life events on mental health and excessive alcohol consumption.