By Scientia Professor Robert Kohn

Book Chapters

Tran M; Nott D; Kohn R, 2022, 'Variational Bayes', in , Wiley, pp. 1 - 9, http://dx.doi.org/10.1002/9781118445112.stat08387

Giordani P; Pitt M; Kohn R, 2012, 'Bayesian Inference for Time Series State Space Models', in The Oxford Handbook of Bayesian Econometrics, http://dx.doi.org/10.1093/oxfordhb/9780199559084.013.0004

Kohn R; Li F; Villani M, 2011, 'Modelling Conditional Densities Using Finite Smooth Mixtures', in Mengersen KL; Robert CP; Titterington DM (ed.), Mixtures: Estimation and Applications, John Wiley & Sons, Ltd, United Kingdom, pp. 123 - 144, http://dx.doi.org/10.1002/9781119995678.ch6

Carter CK; Kohn R; Cripps EJ, 2005, 'Variable Selection and Covariance Selection in Multivariate Regression Models', in

Smith MM; Kohn R; Yau P, 2000, 'Nonparametric Bayesian bivariate surface estimation', in Smoothing and regression: approaches, computation and its application, Wiley & Sons, New York, pp. 545 - 580

Kohn R; Schimek MG; Smith MM, 2000, 'Spline and kernel regression for dependant data', in Smoothing and regression: approaches, computation and its application, Wiley & Sons, New York, pp. 135 - 158

Smith MM; Kohn R, 1998, 'Nonparametric estimation of irregular functions with independent or autocorrelated errors', in Lecture notes in statistics: Practical nonparametric and semiparametric Bayesian statistics, Springer Publishing Company, pp. 157 - 171

Sheather SJ; Wand MP; Smith MS; Kohn R, 1996, 'Comments', in Contributions to Statistics, Physica-Verlag HD, pp. 93 - 102, http://dx.doi.org/10.1007/978-3-642-48425-4_7

Sheather SJ; Wand MP; Smith MM; Kohn R, 1996, 'Comments on the papers by Marron, Cleveland and Loader, Seifert and Gasser', in Statistical theory and computational aspects of smoothing, Physica-Verlag, Heidelberg, pp. 93 - 102

Carter CK; Kohn R, 1996, 'Robust Bayesian nonparametic regression', in Statistical theory and computational aspects of smoothing, Physica-Verlag, Heidelberg, pp. 128 - 148

KOHN R; ANSLEY CF, 1989, 'FILTERING AND SMOOTHING ALGORITHMS FOR STATE SPACE MODELS', in System-Theoretic Methods in Economic Modelling II, Elsevier, pp. 515 - 528, http://dx.doi.org/10.1016/b978-0-08-037932-6.50007-6

Ansley CF; Kohn R, 1984, 'New Algorithmic Developments for Estimation Problems in Time Series', in Compstat 1984, Physica-Verlag HD, pp. 23 - 34, http://dx.doi.org/10.1007/978-3-642-51883-6_2

Ansley CF; Kohn R, 1984, 'On the estimation of ARIMA Models with Missing Values', in Lecture Notes in Statistics, Springer New York, pp. 9 - 37, http://dx.doi.org/10.1007/978-1-4684-9403-7_2

Journal articles

Liu C; Wang C; Tran MN; Kohn R, 2025, 'A long short-term memory enhanced realized conditional heteroskedasticity model', Economic Modelling, 142, http://dx.doi.org/10.1016/j.econmod.2024.106922

Villani M; Quiroz M; Kohn R; Salomone R, 2024, 'Spectral Subsampling MCMC for Stationary Multivariate Time Series with Applications to Vector ARTFIMA Processes', Econometrics and Statistics, 32, pp. 98 - 121, http://dx.doi.org/10.1016/j.ecosta.2022.10.001

Gunawan D; Kohn R; Nott D, 2024, 'Flexible Variational Bayes Based on a Copula of a Mixture', Journal of Computational and Graphical Statistics, 33, pp. 665 - 680, http://dx.doi.org/10.1080/10618600.2023.2262080

Gunawan D; Carter C; Kohn R, 2024, 'Particle MCMC and the correlated particle hybrid sampler for state space models', Journal of Econometrics, http://dx.doi.org/10.1016/j.jeconom.2024.105731

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, 33, pp. 1329 - 1338, http://dx.doi.org/10.1080/10618600.2024.2319159

Gunawan D; Chatterjee P; Kohn R, 2024, 'The Block-Correlated Pseudo Marginal Sampler for State Space Models', Journal of Business and Economic Statistics, 42, pp. 1276 - 1288, http://dx.doi.org/10.1080/07350015.2024.2308109

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

Nguyen TN; Tran MN; Gunawan D; Kohn R, 2023, 'A Statistical Recurrent Stochastic Volatility Model for Stock Markets', Journal of Business and Economic Statistics, 41, pp. 414 - 428, http://dx.doi.org/10.1080/07350015.2022.2028631

Frazier DT; Nott DJ; Drovandi C; Kohn R, 2023, 'Bayesian Inference Using Synthetic Likelihood: Asymptotics and Adjustments', Journal of the American Statistical Association, 118, pp. 2821 - 2832, http://dx.doi.org/10.1080/01621459.2022.2086132

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

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

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, 29, pp. 219 - 241, http://dx.doi.org/10.1037/met0000458

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

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

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', , 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

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