By Scientia Professor Robert Kohn

Journal articles

Gunawan D; Chatterjee P; Kohn R, 2024, 'The Block-Correlated Pseudo Marginal Sampler for State Space Models', Journal of Business & Economic Statistics, pp. 1 - 13, http://dx.doi.org/10.1080/07350015.2024.2308109

Salomone R; Yu X; Nott DJ; Kohn R, 2024, 'Structured Variational Approximations with Skew Normal Decomposable Graphical Models and Implicit Copulas', Journal of Computational and Graphical Statistics, http://dx.doi.org/10.1080/10618600.2024.2319159

Botha I; Kohn R; South L; Drovandi C, 2023, 'Automatically adapting the number of state particles in SMC ²', Statistics and Computing, 33, http://dx.doi.org/10.1007/s11222-023-10250-2

Munezero P; Villani M; Kohn R, 2023, 'Dynamic Mixture of Experts Models for Online Prediction', Technometrics, 65, pp. 257 - 268, http://dx.doi.org/10.1080/00401706.2022.2146755

Gunawan D; Kohn R; Nott D, 2023, 'Flexible Variational Bayes Based on a Copula of a Mixture', Journal of Computational and Graphical Statistics, http://dx.doi.org/10.1080/10618600.2023.2262080

Quiroz M; Nott DJ; Kohn R, 2023, 'Gaussian Variational Approximations for High-dimensional State Space Models', Bayesian Analysis, 18, pp. 989 - 1016, http://dx.doi.org/10.1214/22-BA1332

Nguyen TN; Tran MN; Kohn R, 2022, 'Recurrent conditional heteroskedasticity', Journal of Applied Econometrics, 37, pp. 1031 - 1054, http://dx.doi.org/10.1002/jae.2902

Oliveira R; Scalzo R; Kohn R; Cripps S; Hardman K; Close J; Taghavi N; Lemckert C, 2022, 'Bayesian optimization with informative parametric models via sequential Monte Carlo', Data-Centric Engineering, 3, http://dx.doi.org/10.1017/dce.2022.5

Nguyen TN; Tran MN; Gunawan D; Kohn R, 2022, 'A Statistical Recurrent Stochastic Volatility Model for Stock Markets', Journal of Business and Economic Statistics, http://dx.doi.org/10.1080/07350015.2022.2028631

Frazier DT; Nott DJ; Drovandi C; Kohn R, 2022, 'Bayesian Inference Using Synthetic Likelihood: Asymptotics and Adjustments', Journal of the American Statistical Association, http://dx.doi.org/10.1080/01621459.2022.2086132

Dao VH; Gunawan D; Tran MN; Kohn R; Hawkins GE; Brown SD, 2022, 'Efficient Selection Between Hierarchical Cognitive Models: Cross-Validation With Variational Bayes', Psychological Methods, http://dx.doi.org/10.1037/met0000458

Gunawan D; Kohn R; Tran MN, 2022, 'Flexible and Robust Particle Tempering for State Space Models', Econometrics and Statistics, http://dx.doi.org/10.1016/j.ecosta.2022.09.003

Villani M; Quiroz M; Kohn R; Salomone R, 2022, 'Spectral Subsampling MCMC for Stationary Multivariate Time Series with Applications to Vector ARTFIMA Processes', Econometrics and Statistics, http://dx.doi.org/10.1016/j.ecosta.2022.10.001

Gunawan D; Hawkins GE; Kohn R; Tran MN; Brown SD, 2022, 'Time-Evolving Psychological Processes Over Repeated Decisions', Psychological Review, 129, pp. 438 - 456, http://dx.doi.org/10.1037/rev0000351

Gunawan D; Kohn R; Nott D, 2021, 'Variational Bayes approximation of factor stochastic volatility models', International Journal of Forecasting, 37, pp. 1355 - 1375, http://dx.doi.org/10.1016/j.ijforecast.2021.05.001

Botha I; Kohn R; Drovandi C, 2021, 'Particle Methods for Stochastic Differential Equation Mixed Effects Models', Bayesian Analysis, 16, pp. 575 - 609, http://dx.doi.org/10.1214/20-BA1216

Tran MN; Scharth M; Gunawan D; Kohn R; Brown SD; Hawkins GE, 2021, 'Robustly estimating the marginal likelihood for cognitive models via importance sampling', Behavior Research Methods, 53, pp. 1148 - 1165, http://dx.doi.org/10.3758/s13428-020-01348-w

Dao V-H; Gunawan D; Tran M-N; Kohn R; Hawkins GE; Brown SD, 2021, 'Efficient Selection Between Hierarchical Cognitive Models: Cross-validation With Variational Bayes', , http://arxiv.org/abs/2102.06814v2

Wall L; Gunawan D; Brown SD; Tran MN; Kohn R; Hawkins GE, 2021, 'Identifying relationships between cognitive processes across tasks, contexts, and time', Behavior Research Methods, 53, pp. 78 - 95, http://dx.doi.org/10.3758/s13428-020-01405-4

Quiroz M; Tran MN; Villani M; Kohn R; Dang KD, 2021, 'The Block-Poisson Estimator for Optimally Tuned Exact Subsampling MCMC', Journal of Computational and Graphical Statistics, 30, pp. 877 - 888, http://dx.doi.org/10.1080/10618600.2021.1917420

Gunawan D; Dang KD; Quiroz M; Kohn R; Tran MN, 2020, 'Subsampling sequential Monte Carlo for static Bayesian models', Statistics and Computing, 30, pp. 1741 - 1758, http://dx.doi.org/10.1007/s11222-020-09969-z

Gunawan D; Hawkins GE; Tran MN; Kohn R; Brown SD, 2020, 'New estimation approaches for the hierarchical Linear Ballistic Accumulator model', Journal of Mathematical Psychology, 96, http://dx.doi.org/10.1016/j.jmp.2020.102368

Chin V; Gunawan D; Fiebig DG; Kohn R; Sisson SA, 2020, 'Efficient data augmentation for multivariate probit models with panel data: an application to general practitioner decision making about contraceptives', Journal of the Royal Statistical Society. Series C: Applied Statistics, 69, pp. 277 - 300, http://dx.doi.org/10.1111/rssc.12393

Kohn R; Mendes E; Gunawan D; Carter C, 2020, 'A flexible particle Markov chain Monte Carlo method', Statistics and Computing, http://dx.doi.org/10.1007/s11222-019-09916-7

Tran MN; Nguyen N; Nott D; Kohn R, 2020, 'Bayesian Deep Net GLM and GLMM', Journal of Computational and Graphical Statistics, 29, pp. 97 - 113, http://dx.doi.org/10.1080/10618600.2019.1637747

Gunawan D; Khaled MA; Kohn R, 2020, 'Mixed Marginal Copula Modeling', Journal of Business and Economic Statistics, 38, pp. 137 - 147, http://dx.doi.org/10.1080/07350015.2018.1469998

Gunawan D; Tran MN; Suzuki K; Dick J; Kohn R, 2019, 'Computationally efficient Bayesian estimation of high-dimensional Archimedean copulas with discrete and mixed margins', Statistics and Computing, 29, pp. 933 - 946, http://dx.doi.org/10.1007/s11222-018-9846-y

Dang KD; Quiroz M; Kohn R; Tran MN; Villani M, 2019, 'Hamiltonian monte carlo with energy conserving subsampling', Journal of Machine Learning Research, 20

Quiroz M; Kohn R; Villani M; Tran MN, 2019, 'Speeding Up MCMC by Efficient Data Subsampling', Journal of the American Statistical Association, 114, pp. 831 - 843, http://dx.doi.org/10.1080/01621459.2018.1448827

Quiroz M; Villani M; Kohn R; Tran MN; Dang KD, 2018, 'Subsampling MCMC - an Introduction for the Survey Statistician', Sankhya A, 80, pp. 33 - 69, http://dx.doi.org/10.1007/s13171-018-0153-7

Quiroz M; Nott DJ; Kohn R, 2018, 'Gaussian variational approximation for high-dimensional state space models', , http://arxiv.org/abs/1801.07873v3

Quiroz M; Tran MN; Villani M; Kohn R, 2018, 'Speeding up MCMC by Delayed Acceptance and Data Subsampling', Journal of Computational and Graphical Statistics, 27, pp. 12 - 22, http://dx.doi.org/10.1080/10618600.2017.1307117

Tran MN; Nott DJ; Kohn R, 2017, 'Variational Bayes With Intractable Likelihood', Journal of Computational and Graphical Statistics, 26, pp. 873 - 882, http://dx.doi.org/10.1080/10618600.2017.1330205

Khaled MA; Kohn R, 2017, 'On approximating copulas by finite mixtures', On approximating copulas by finite mixtures, http://arxiv.org/abs/1705.10440v3

Del Moral P; Kohn R; Patras F, 2016, 'On particle Gibbs samplers', Annales de l'institut Henri Poincare (B) Probability and Statistics, 52, pp. 1687 - 1733, http://dx.doi.org/10.1214/15-AIHP695

Tran MN; Nott DJ; Kuk AYC; Kohn R, 2016, 'Parallel Variational Bayes for Large Datasets With an Application to Generalized Linear Mixed Models', Journal of Computational and Graphical Statistics, 25, pp. 626 - 646, http://dx.doi.org/10.1080/10618600.2015.1012293

Tran M-N; Kohn R; Quiroz M; Villani M, 2016, 'The Block Pseudo-Marginal Sampler', The Block Pseudo-Marginal Sampler, http://arxiv.org/abs/1603.02485v5

Scharth M; Kohn R, 2016, 'Particle efficient importance sampling', Journal of Econometrics, 190, pp. 133 - 147, http://dx.doi.org/10.1016/j.jeconom.2015.03.047

Tran MN; Kohn R, 2015, 'Exact ABC using Importance Sampling', , http://arxiv.org/abs/1509.08076v1

Pitt MK; Hall J; Kohn R, 2015, 'Bayesian inference for latent factor GARCH models', Bayesian inference for latent factor GARCH models, http://arxiv.org/abs/1507.01179v1

Doucet A; Pitt MK; Deligiannidis G; Kohn R, 2015, 'Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator', Biometrika, 102, pp. 295 - 313, http://dx.doi.org/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, pp. 465 - 469, http://dx.doi.org/10.1016/j.crma.2015.02.008

Peters GW; Dong AXD; Kohn R, 2014, 'A copula based Bayesian approach for paid-incurred claims models for non-life insurance reserving', Insurance: Mathematics and Economics, 59, pp. 258 - 278, http://dx.doi.org/10.1016/j.insmatheco.2014.09.011

Kohn R; tran ; giordani ; pitt ; mun , 2014, 'Copula-Type Estimators for Flexible Multivariate Density Modeling Using Mixtures', Journal of Computational and Graphical Statistics, 23, pp. 1163 - 1178, http://dx.doi.org/10.1080/10618600.2013.842918

Kohn R; Tran M; Pitt MK, 2014, 'Adaptive Metropolis–Hastings sampling using reversible dependent mixture proposals', Statistics and Computing, 25, http://dx.doi.org/10.1007/s11222-014-9509-6

Hall J; Pitt MK; Kohn R, 2014, 'Bayesian inference for nonlinear structural time series models', Journal of Econometrics, 179, pp. 99 - 111, http://dx.doi.org/10.1016/j.jeconom.2013.10.016

Tran M; Giordani P; Mun X; Kohn R, 2013, 'Flexible multivariate density estimation with marginal adaptation', Journal of Computational and Graphical Statistics, 22, pp. 814 - 829, http://dx.doi.org/10.1080/10618600.2012.672784

Tran M-N; Scharth M; Pitt MK; Kohn R, 2013, 'Importance sampling squared for Bayesian inference in latent variable models', Importance sampling squared for Bayesian inference in latent variable models, http://arxiv.org/abs/1309.3339v4

Giordani P; Mun X; Kohn R, 2012, 'Efficient estimation of covariance matrices using posterior mode multiple shrinkage', Journal of Financial Econometrics, 11, pp. 154 - 192, http://dx.doi.org/10.1093/jjfinec/nbs009

Tran MN; Nott DJ; Kohn R, 2012, 'Simultaneous variable selection and component selection for regression density estimation with mixtures f heteroscedastic experts', Electronic Journal of Statistics, 6, pp. 1170 - 1199, http://dx.doi.org/10.1214/12-EJS705

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