Robert Kohn | ARC Centre of Excellence for Mathematical and Statistical Frontiers

UNSW Sydney

Research Interests:

Bayesian methodolgy

Bayesian nonparametric methods

Markov chain Monte Carlo simulation algorithms

Model averaging

Multivariate Gaussian and non-Gaussian regression

Nonparametric regression

Time series modelling

Variable selection

Projects

Developing flexible copula based methods

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.

Enabling Bayesian inference for big data

In the last decade or so, there has been a dramatic increase in storage facilities and the possibility of processing huge amounts of data. This has made large high-quality data sets widely accessible for practitioners. This technology innovation seriously challenges inference methodology, in particular simulation algorithms commonly applied in Bayesian inference. These algorithms typically require repeated evaluations over the whole data set when fitting models, precluding their use in the age of so called big data.

Flexible Bayesian Methods for time series and longitudinal panel data models

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.

Flexible models and methods for cognitive model-based decision making

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.

Publications

Invited talks, refereed proceedings and other conference outputs

2018

Quiroz, M., Nott D., & Kohn R. (2018). Gaussian variational approximation for high-dimensional state space models. 2nd International Conference on Econometrics and Statistics.

Quiroz, M., Tran M N., Villani M., Kohn R., & Dang K-D. (2018). The block-Poisson estimator for optimally exact subsampling MCMC. International Society of Bayesian Analysis.

Quiroz, M., Nott D., & Kohn R. (2018). The Mathematics of Biological Systems Management Symposium. The Mathematics of Biological Systems Management Symposium.

Dang, K.. D., Quiroz M., Kohn R., Tran M N., & Villani M. (2018). Hamiltonian Monte Carlo with energy conserving subsampling. The 2nd International Conference on Econometrics and Statistics (EcoSta 2018).

2017

Quiroz, M., Tran M N., Villani M., & Kohn R. (2017). Exact Subsampling MCMC. 1st International Conference on Econometrics and Statistics (EcoSta 2017).

Tran, M N., Kohn R., Quiroz M., & Villani M. (2017). The block-pseudo marginal sampler. The 1st International Conference on Econometrics and Statistics (EcoSta 2017).

Dang, K.-D., Quiroz M., Kohn R., Tran M N., & Villani M. (2017). Hamiltonian Monte Carlo with Energy Conserving Subsampling. Bayes on the Beach.

Tran, M N., Kohn R., Quiroz M., & Villani M. (2017). The block pseudo-marginal sampler. Bayes on the beach.

Quiroz, M., Nott D., & Kohn R. (2017). Gaussian variational approximation for high-dimensional state space models. The Sydney Time Series & Forecasting Symposium.

2016

Kohn, R. (2016). Speeding Up MCMC by Efficient Data Subsampling. ISBA 2016 World Meeting, Sardinia .

Journal Articles

In Press

Quiroz, M., Kohn R., Villani M., & Tran M N. (In Press). Speeding Up MCMC by Efficient Data Subsampling. Journal of the American Statistical Association. 1 - 13. doi: 10.1080/01621459.2018.1448827

2018

Quiroz, M., Nott D. J., & Kohn R. (2018). Gaussian variational approximations for high-dimensional state space models.

2017

Tran, M N., Nott D. J., & Kohn R. (2017). Variational Bayes with Intractable Likelihood. Journal of Computational and Graphical Statistics. 26(4), doi: 10.1080/10618600.2017.1330205

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.

Tran, M N., Nott D. J., & Kohn R. (2017). Variational Bayes With Intractable Likelihood. Journal of Computational and Graphical Statistics. 26(4), 873 - 882. doi: 10.1080/10618600.2017.1330205

Tran, M N., Nott D.. J., & Kohn R. (2017). Variational Bayes with intractable likelihood. Journal of Computational and Graphical Statistics. 26(4), doi: 10.1080/10618600.2017.1330205

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.

Dang, K.. D., Quiroz M., Kohn R., Tran M N., & Villani M. (2017). Hamiltonian Monte Carlo with Energy Conserving Subsampling.

Gunawan, D., Khaled M., & Kohn R. (2017). Mixed Marginal Copula Modeling.

Gunawan, D., Carter C., & Kohn R. (2017). Efficient Bayesian inference for multivariate factor stochastic volatility models with leverage.

Khaled, M., & Kohn R. (2017). The approximation properties of copulas by mixtures.

2016

Tran, M N., Nott D., Kuk A., & Kohn R. (2016). Parallel variational Bayes for large datasets with an application to generalized linear mixed models. Journal of Computational and Graphical Statistics. 25(2), 626-646. doi: 10.1080/10618600.2015.1012293

Tran, M N., Pitt M. K., & Kohn R. (2016). Adaptive Metropolis–Hastings sampling using reversible dependent mixture proposals. Statistics and Computing. 26(1-2), 361-381. doi: 10.1007/s11222-014-9509-6

Del Moral, P., Kohn R., & Patras F. (2016). On particle Gibbs samplers. Annales de l'IHP-Probabilités et Statistiques. 52(4), 1687-1733. doi: 10.1214/15-AIHP695

Gunawan, D., Tran M N., Suzuki K.., Dick J.., & Kohn R. (2016). Computationally efficient Bayesian estimation of high dimensional copulas with discrete and mixed margins.

Scharth, M., & Kohn R. (2016). Particle efficient importance sampling. Journal of Econometrics. 190(1), 133-147. doi: 10.1016/j.jeconom.2015.03.047

2015

Doucet, A., Pitt M. K., Deligiannidis G., & Kohn R. (2015). Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator. Biometrika. 102(2), 295-313. doi: 10.1093/biomet/asu075

Del Moral, P., Kohn R., & Patras F. (2015). A duality formula for Feynman–Kac path particle models. Comptes Rendus Mathematique. 353(5), 465-469. doi: 10.1016/j.crma.2015.02.008

2014

Peters, G. W., Dong A. X. D., & Kohn R. (2014). A copula based Bayesian approach for paid–incurred claims models for non-life insurance reserving. Insurance: Mathematics and Economics. 59, 258-278. doi: 10.1016/j.insmatheco.2014.09.011

Tran, M N., Giordani P., Mun X., Kohn R., & Pitt M. K. (2014). Copula-Type Estimators for Flexible Multivariate Density Modeling Using Mixtures. Journal of Computational and Graphical Statistics. 23(4), 1163-1178. doi: 10.1080/10618600.2013.842918

Technical reports and unrefereed outputs

2017

Gunawan, D., Carter C., & Kohn R. (2017). Efficient Bayesian inference for multivariate factor stochastic volatility models with leverage.

Dang, K.-D., Quiroz M., Kohn R., Tran M N., & Villani M. (2017). Hamiltonian Monte Carlo with Energy Conserving Subsampling.

2016

Gunawan, D., Tran M N., Suzuki K., Dick J., & Kohn R. (2016). Computationally Efficient Bayesian Estimation of High Dimensional Copulas with Discrete and Mixed Margins.

Tran, M N., Kohn R., Quiroz M., & Villani M. (2016). Block-Wise Pseudo-Marginal Metropolis-Hastings.

Quiroz, M., Villani M., & Kohn R. (2016). Exact Subsampling MCMC.

Gunawan, D., Tran M N., & Kohn R. (2016). Fast Inference for Intractable Likelihood Problems using Variational Bayes.

Gunawan, D., Khaled M., & Kohn R. (2016). Mixed Marginal Copula Modeling.

Research links

ORCID

Contact

r.kohn@unsw.edu.au

Welcome to the ACEMS Reportal

All contributors should enter data on all of the Centre’s reportable key performance indicators (KPIs).

Professor Robert Kohn

Projects

Developing flexible copula based methods

Enabling Bayesian inference for big data

Flexible Bayesian Methods for time series and longitudinal panel data models

Flexible models and methods for cognitive model-based decision making

Publications

2018

2017

2016

In Press

2018

2017

2016

2015

2014

2017

2016

Research links

Contact

Links

ARC Centre of Excellence for Mathematical & Statistical Frontiers

Welcome to the ACEMS Reportal

All contributors should enter data on all of the Centre’s reportable key performance indicators (KPIs).

Professor Robert Kohn

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Links

ARC Centre of Excellence for Mathematical & Statistical Frontiers