David Gunawan
David Gunawan
Verified email at unsw.edu.au
Title
Cited by
Cited by
Year
Subsampling sequential Monte Carlo for static Bayesian models
D Gunawan, KD Dang, M Quiroz, R Kohn, MN Tran
arXiv preprint arXiv:1805.03317, 2018
122018
Fast inference for intractable likelihood problems using variational Bayes
D Gunawan, MN Tran, R Kohn
arXiv preprint arXiv:1705.06679, 2017
72017
A flexible particle Markov chain Monte Carlo method
EF Mendes, CK Carter, D Gunawan, R Kohn
Statistics and Computing, 1-16, 2020
62020
Mixed marginal copula modeling
D Gunawan, MA Khaled, R Kohn
Journal of Business & Economic Statistics 38 (1), 137-147, 2020
52020
Computationally efficient Bayesian estimation of high dimensional copulas with discrete and mixed margins
D Gunawan, MN Tran, K Suzuki, J Dick, R Kohn
arXiv preprint arXiv:1608.06174, 2016
52016
A long short-term memory stochastic volatility model
N Nguyen, MN Tran, D Gunawan, R Kohn
arXiv preprint arXiv:1906.02884, 2019
32019
Bayesian inference for health inequality and welfare using qualitative data
D Gunawan, WE Griffiths, D Chotikapanich
Economics Letters 162, 76-80, 2018
32018
Bayesian inference for state space models using block and correlated pseudo marginal methods
P Choppala, D Gunawan, J Chen, MN Tran, R Kohn
arXiv preprint arXiv:1612.07072, 2016
32016
New estimation approaches for the linear ballistic accumulator model
D Gunawan, S Brown, R Kohn, MN Tran
arXiv preprint arXiv:1806.10089, 2018
22018
Bayesian Weighted Inference from Surveys
D Gunawan, A Panagiotelis, W Griffiths, D Chotikapanich
Technical report, University of Melbourne, 2017
22017
Bayesian assessment of Lorenz and stochastic dominance using a mixture of gamma densities
D Lander, D Gunawan, WE Griffiths, D Chotikapanich
University of Melbourne, Department of Economics, 2016
22016
Bayesian assessment of Lorenz and stochastic dominance using a mixture of gamma densities
D Lander, D Gunawan, WE Griffiths, D Chotikapanich
University of Melbourne, Department of Economics, 2016
22016
Flexible particle markov chain monte carlo methods with an application to a factor stochastic volatility model
EF Mendes, CK Carter, D Gunawan, R Kohn
arXiv preprint arXiv:1401.1667, 2014
22014
Computationally efficient Bayesian estimation of high-dimensional Archimedean copulas with discrete and mixed margins
D Gunawan, MN Tran, K Suzuki, J Dick, R Kohn
Statistics and Computing 29 (5), 933-946, 2019
12019
Robustly estimating the marginal likelihood for cognitive models via importance sampling
MN Tran, M Scharth, D Gunawan, R Kohn, SD Brown, GE Hawkins
arXiv preprint arXiv:1906.06020, 2019
12019
Efficient data augmentation for multivariate probit models with panel data: an application to general practitioner decision making about contraceptives
V Chin, D Gunawan, DG Fiebig, R Kohn, SA Sisson
Journal of the Royal Statistical Society: Series C (Applied Statistics), 2018
12018
Flexible density tempering approaches for state space models with an application to factor stochastic volatility models
D Gunawan, R Kohn, C Carter, MN Tran
arXiv preprint arXiv:1805.00649, 2018
12018
Efficiently Combining Pseudo Marginal and Particle Gibbs Sampling
D Gunawan, C Carter, R Kohn
arXiv preprint arXiv:1804.04359, 2018
12018
Efficient Bayesian estimation for flexible panel models for multivariate outcomes: Impact of life events on mental health and excessive alcohol consumption
D Gunawan, D Fiebig, R Kohn
arXiv preprint arXiv:1706.03953, 2017
12017
Bayesian assessment of Lorenz and stochastic dominance
D Lander, D Gunawan, W Griffiths, D Chotikapanich
The University of Melbourne Department of Economics-Working Papers Series, 2017
12017
The system can't perform the operation now. Try again later.
Articles 1–20