People_

Professor Jennifer Chan

Professor

Phone

9351 4873

Fax

9351 4534

Email

jennifer.chan@sydney.edu.au

Address

F07 - Carslaw Building

The University of Sydney

Biographical details

Jennifer Chan did her PhD at The University of New South Wales, Sydney and was graduated in 1997. From 1996, she lectured at the University of Hong Kong before joining the Statistics Department in 2006.

Research interests

Generalized Linear Mixed Model, Bayesian robustness, heavy tail distributions, scale mixture distributions, geometric process for time series data, drop-out models, quantile regression, volatility models for risk management, neural networks for portfolio management, applications for insurance and financial data.

I am a member of the Statistics Research group.

Teaching and supervision

Timetable

Current projects

1. Predictive models for auto-insurance claims and drivers classification.

2. Models for the time series of covariance matrices for risk management.

3. Neural networks for quantile regression in financial time series and extension.

4. Neural networks for market returns and portfolio management.

5. Hybrid statistical and neural network models.

Awards and honours

Awards

1. The second prize (Third Contributor) of The Natural Science Award of the Ministry of Education, People Republic of China in 2008 based on her work in "Geometric Process" with the University of Hong Kong being the second completion institute.

2. The National Drug Strategy (NDS) Research Scholarship from Commonwealth Department of Human Services and Health, Australia in 1994-1996.

International links

Israel	(University of Haifa) with Professor Udi Makov in Actuarial Research Center
Japan	(Institute of Statistical Mathematics, Tokyo, Japan) with Professor Tomoko Matsui
Malaysia	(University of Malaya) with Professor Kok Haur Ng, Institute of Mathematical Sciences
United States	(University of California, Santa Barbara) with Professor Gareth Peters in the Department of Statistics and Applied Probability

PhD and master's project opportunities

Multivariate volatility models for high frequency data

Analysis of telematics data using machine learning techniques

Current research students

Project title	Research student
Semi-metric machine learning and deep learning techniques to investigate the value at risk in financial market	Nia CHEN
Machine Learning / Deep Learning in Asset Management	Fong LAM
Using Machine Learning to Solve Stochastic Differential Equations	Nick MILTCHINOV

Selected publications

MATH1015: Biostatistics

(Cengage Learning, 2013)

Publications

By Type

Expand all

Books

Peiris, M., Chan, J., Jajo, N. (2021). A Quick Reference Guide to Beginners of Statistics and Data Science Using RStudio. Indonesia: CV. Meugah Printindo.
Rosner, B., Peiris, M., Chan, J., Marchev, D. (2013). MATH1015: Biostatistics. Sydney: Cengage Learning.

Book Chapters

Yatigammana, R., Choy, S., Chan, J. (2016). Autoregressive Conditional Duration Model with an Extended Weibull Error Distribution. In Van-Nam Huynh, Vladik Kreinovich, Songsak Sriboonchitta (Eds.), Causal Inference in Econometrics, (pp. 83-107). Cham: Springer. [More Information]
Chan, J., Lam, C., Choy, S. (2014). An Innovative Financial Time Series Model: The Geometric Process Model. In Van-Nam Huynh, Vladik Kreinovich, Songsak Sriboonchitta (Eds.), Modeling Dependence in Econometrics, (pp. 81-99). Cham: Springer. [More Information]

Journals

Wong, S., Chan, J., Azizi, L. (2025). Quantifying neural network uncertainty under volatility clustering. Neurocomputing, 614, 128816 (14 pages). [More Information]
Usman, F., Chan, J., Makov, U., Wang, Y., Dong, A. (2023). Claim Prediction and Premium Pricing for Telematics Auto Insurance Data Using Poisson Regression with Lasso Regularisation. Risks, 12(137), 33 pages. [More Information]
Nitithumbundit, T., Chan, J. (2023). Maximum leave-one-out likelihood method for the location parameter of variance gamma distribution with unbounded density. Journal of Statistical Computation and Simulation, 93(15), 2642-2671. [More Information]
Tan, S., Ng, K., Chan, J. (2023). Predicting Returns, Volatilities and Correlations of Stock Indices Using Multivariate Conditional Autoregressive Range and Return Models. Mathematics, 11 Open Access(1), 13-1-13-24. [More Information]
James, N., Menzies, M., Chan, J. (2023). Semi-Metric Portfolio Optimization: A New Algorithm Reducing Simultaneous Asset Shocks. Econometrics, 11 (Open Access)(1), Article no. 8 - 1-Article no. 8 - 33. [More Information]
Nitithumbundit, T., Chan, J. (2022). Covid-19 impact on Cryptocurrencies market using Multivariate Time Series Models. Quarterly Review of Economics and Finance, 86, 365-375. [More Information]
Peters, G., Yan, H., Chan, J. (2022). Model risk in mortality-linked contingent claims pricing. Journal of Risk Model Validation, 16(3), 1-53. [More Information]
Usman, F., Chan, J. (2022). New loss reserve models with persistence effects to forecast trapezoidal losses in run-off triangles. Astin Bulletin, 52(3), 877-920. [More Information]
Wong, S., Chan, J., Azizi, L., Xu, R. (2022). Time-varying neural network for stock return prediction. Intelligent Systems in Accounting, Finance and Management, 29(1), 3-18. [More Information]
Chan, J., Choy, S., Makov, U., Shamir, A., Shapovalov, V. (2022). Variable Selection Algorithm for a Mixture of Poisson Regression for Handling Overdispersion in Claims Frequency Modeling Using Telematics Car Driving Data. Risks, 10 (Open Access)(4), Article No 83. [More Information]
James, N., Menzies, M., Chan, J. (2021). Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19. Physica A, 565, 125581. [More Information]
Nitithumbundit, T., Chan, J. (2021). ECM algorithm for estimating vector ARMA model with variance gamma distribution and possible unbounded density. Australian & New Zealand Journal of Statistics, 63(3), 485-516. [More Information]
Tan, S., Chan, J., Ng, K. (2021). Modelling and forecasting stock volatility and return: a new approach based on quantile Rogers-Satchell volatility measure with asymmetric bilinear CARR model. Studies in Nonlinear Dynamics and Econometrics, In Press, 1-38. [More Information]
Yan, H., Peters, G., Chan, J. (2021). Mortality models incorporating long memory for life table estimation: A comprehensive analysis. Annals of Actuarial Science, 15(3), 567-604. [More Information]
Peters, G., Yan, H., Chan, J. (2021). Statistical features of persistence and long memory in mortality data. Annals of Actuarial Science, 15(2), 291-317. [More Information]
Wang, J., Chan, J. (2021). Stochastic modelling of volatility and interrelationships in the Australian electricity markets. Communications in Statistics: Simulation and Computation, In Press, 1-21. [More Information]
Nitithumbundit, T., Chan, J. (2020). ECM Algorithm for Auto-Regressive Multivariate Skewed Variance Gamma Model with Unbounded Density. Methodology and Computing in Applied Probability, 22(3), 1169-1191. [More Information]
Yan, H., Peters, G., Chan, J. (2020). Multivariate Long-Memory Cohort Mortality Models. Astin Bulletin, 50(1), 223-263. [More Information]
James, N., Menzies, M., Azizi, L., Chan, J. (2020). Novel semi-metrics for multivariate change point analysis and anomaly detection. Physica D: Nonlinear Phenomena, 412(November 2020), 132636 - 1-132636 - 15. [More Information]
Phillip, A., Chan, J., Peiris, M. (2020). On generalized bivariate student-t Gegenbauer long memory stochastic volatility models with leverage: Bayesian forecasting of cryptocurrencies with a focus on Bitcoin. Econometrics and Statistics, 16, 69-90. [More Information]
Chan, J., Choy, S., Walker, S. (2020). On the estimation of the shape parameter of a symmetric distribution. Journal of Data Science, 18(1), 78-100. [More Information]
Tan, S., Chan, J., Ng, K. (2020). On the speculative nature of cryptocurrencies: A study on Garman and Klass volatility measure. Finance Research Letters, 32, 101075. [More Information]
Chan, J., Ng, K., Ragell, R. (2019). Bayesian return forecasts using realised range and asymmetric CARR model with various distribution assumptions. International Review of Economics and Finance, 61, 188-212. [More Information]
Chan, J., Ng, K., Nitithumbundit, T., Peiris, M. (2019). Efficient estimation of financial risk by regressing the quantiles of parametric distributions: An application to CARR models. Studies in Nonlinear Dynamics and Econometrics, 23(2), 1-22. [More Information]
Yatigammana, R., Chan, J., Gerlach, R. (2019). Forecasting trade durations via ACD models with mixture distributions. Quantitative Finance, 19(12), 2051-2067. [More Information]
Phillip, A., Chan, J., Peiris, M. (2019). On long memory effects in the volatility measure of Cryptocurrencies. Finance Research Letters, 28, 95-100. [More Information]
Tan, S., Ng, K., Chan, J., Mohamed, I. (2019). Quantile range-based volatility measure for modelling and forecasting volatility using high frequency data. North American Journal of Economics and Finance, 47, 537-551. [More Information]
Phillip, A., Chan, J., Peiris, M. (2018). A new look at Cryptocurrencies. Economics Letters, 163, 6-9. [More Information]
Phillip, A., Chan, J., Peiris, M. (2018). Bayesian estimation of Gegenbauer long memory processes with stochastic volatility: methods and applications. Studies in Nonlinear Dynamics and Econometrics, 22(3), 1-29. [More Information]
Chan, J., Choy, S., Makov, U., Landsman, Z. (2018). Modelling Insurance Losses Using Contaminated Generalised Beta Type-II Distribution. Astin Bulletin, 48(2), 871-904. [More Information]
Ng, K., Peiris, M., Chan, J., Allen, D., Ng, K. (2017). Efficient modelling and forecasting with range based volatility models and its application. North American Journal of Economics and Finance, 42, 448-460. [More Information]
Chan, J., Wan, W. (2016). Bayesian analysis of Cannabis offences using generalized Poisson geometric process model with flexible dispersion. Journal of Statistical Computation and Simulation, 86(16), 3315-3336. [More Information]
Chan, J. (2016). Bayesian informative dropout model for longitudinal binary data with random effects using conditional and joint modeling approaches. Biometrical Journal, 58(3), 549-569. [More Information]
Choy, S., Chan, J., Makov, U. (2016). Robust Bayesian analysis of loss reserving data using scale mixtures distributions. Journal of Applied Statistics, 43(3), 396-411. [More Information]
Chan, J. (2015). Predicting loss reserves using quantile regression Running title: Quantile regression loss reserve models. Journal of Data Science, 13(1), 127-156.
Dong, A., Chan, J., Peters, G. (2015). Risk margin quantile function via parametric and non-parametric Bayesian approaches. Astin Bulletin, 45(3), 503-550. [More Information]
Chan, J., Wan, W., Yu, P. (2014). A Poisson geometric process approach for predicting drop-out and committed first-time blood donors. Journal of Applied Statistics, 41(7), 1486-1503. [More Information]
Chan, J. (2014). Analysis of Correlation Structures using Generalized Estimating Equation Approach for Longitudinal Binary Data. Journal of Data Science, 12, 293-305.
Chan, J., Choy, S., Lam, C. (2014). Modeling Electricity Price Using A Threshold Conditional Autoregressive Geometric Process Jump Model. Communications in Statistics - Theory and Methods, 43(10-12), 2505-2515. [More Information]
Chan, J., Wan, W. (2014). Multivariate generalized Poisson geometric process model with scale mixtures of normal distributions. Journal of Multivariate Analysis, 127, 72-87. [More Information]
Dong, X., Chan, J. (2013). Bayesian analysis of loss reserving using dynamic models with generalized beta distribution. Insurance: Mathematics and Economics, 53, 355-365. [More Information]
Wang, J., Choy, S., Chan, J. (2013). Modelling Stochastic Volatility using Generalized t distribution. Journal of Statistical Computation and Simulation, 83(2), 340-354. [More Information]
Jones, C., Kemp, R., Chan, J. (2013). The relationship between delay discounting, judicial supervision, and substance use among adult drug court clients. Psychology, Public Policy, and Law, 19(4), 454-465. [More Information]
Chan, J., Lam, C., Yu, P., Choy, S., Chen, C. (2012). A Bayesian conditional autoregressive geometric process model for range data. Computational Statistics and Data Analysis, 56(11), 3006-3019. [More Information]
Chen, C., Chan, J., Gerlach, R., Hsieh, W. (2011). A comparison of estimators for regression models with change points. Statistics and Computing, 21(3), 395-414. [More Information]
Wan, W., Chan, J. (2011). Bayesian analysis of robust Poisson geometric process model using heavy-tailed distributions. Computational Statistics and Data Analysis, 55(1), 687-702. [More Information]
Chan, J., Wan, W. (2011). Bayesian approach to analysing longitudinal bivariate binary data with informative dropout. Computational Statistics, 26(1), 121-144. [More Information]
Chen, C., Chan, J., So, M., Lee, K. (2011). Classification in segmented regression problems. Computational Statistics and Data Analysis, 55(7), 2276-2287. [More Information]
Wang, J., Chan, J., Choy, S. (2011). Stochastic Volatility Models with Leverage and Heavy-Tailed Distributions: A Bayesian Approach Using Scale Mixtures. Computational Statistics and Data Analysis, 55(1), 852-862. [More Information]
Chan, J., Leung, D. (2010). Binary geometric process model for the modeling of longitudinal binary data with trend. Computational Statistics, 25, 505-536. [More Information]
Wan, W., Chan, J. (2009). A New Approach for Handling Longitudinal Count Data with Zero-Inflation and Overdispersion: Poisson Geometric Process Model. Biometrical Journal, 51(4), 556-570. [More Information]
Choy, S., Chan, J., Makov, U. (2009). Model selection for loss reserves: The growing triangle technique. Risk, Life and Pensions, 5, 35-40.
Chan, J., Leung, D., Choy, S., Wan, W. (2009). Nonignorable dropout models for longitudinal binary data with random effects: An application of Monte Carlo approximation through the Gibbs output. Computational Statistics and Data Analysis, 53(12), 4530-4545. [More Information]
Chan, J., Choy, S. (2008). Analysis of covariance structures in time series. Journal of Data Science, 6(4), 573-589.
Chan, J., Choy, S. (2008). Analysis of covariance structures in time series. Journal of Data Science, 6(4), 573-589.
Chan, J., Choy, S., Makov, U. (2008). Robust Bayesian Analysis of Loss Reserves Data using the Generalized-t Distribution. Astin Bulletin, 38(1), 207-230. [More Information]
Choy, S., Chan, J. (2008). Scale mixtures distributions in statistical modelling. Australian and New Zealand Journal of Statistics, 50(2), 135-146. [More Information]
Chan, J., Choy, S., Lee, A. (2007). Bayesian analysis of constant elasticity of variance models. Applied Stochastic Models in Business and Industry, 23(1), 83-96. [More Information]
Yu, P., Chung, K., Lin, C., Chan, J., Lee, C. (2007). Predicting potential drop-out and future commitment for first time donors based on first 1.5-year donation patterns: the case in Hong Kong Chinese Donors. Vox Sanguinis, 93(1), 57-63. [More Information]
Chan, J., Yu, P., Lam, Y., Ho, A. (2006). Modelling SARS data using threshold geometric process. Statistics in Medicine, 25(11), 1826-1839. [More Information]
Yu, P., Chan, J., Fung, W. (2006). Statistical Exploration from SARS. The American Statistician, 60(1), 81-91. [More Information]
Chan, J., Kuk, A., Yam, C. (2005). Monte Carlo Approximation through Gibbs output in Generalized linear mixed models. Journal of Multivariate Analysis, 94(2), 300-312. [More Information]
Lam, Y., Zhu, L., Chan, J., Liu, Q. (2004). Analysis of Data from a Series of Events by a Geometric Process Model. Acta Mathematicae Applicatae Sinica, 20(2), 263-282. [More Information]
Chan, J., Yeh, L., Leung, D. (2004). Statistical inference for geometric processes with gamma distributions. Computational Statistics and Data Analysis, 47(3), 565-581. [More Information]
Kuk, A., Chan, J. (2001). Three ways of implementing the EM algorithm when parameters are not identifiable. Biometrical Journal, 43(2), 207-218.
Chan, J. (2000). Initial Stage Problem in Autoregressive Binary Regression. Journal of the Royal Statistical Society: Series D (The Statistician), 49(4), 495-502. [More Information]
Lam, Y., Chan, J. (1998). Statistical inference for geometric processes with lognormal distribution. Computational Statistics and Data Analysis, 27(1), 99-112. [More Information]
Chan, J., Kuk, A., Bell, J., Gilchrist, C. (1998). The Analysis of Methadone Clinic Data Using Marginal and Conditional Logistic Models with Mixture or Random Effects. Australian & New Zealand Journal of Statistics, 40(1), 1-10. [More Information]
Chan, J., Kuk, A., Bell, J. (1997). A likelihood approach to analysing longitudinal bivariate binary data. Biometrical Journal, 39(4), 409-421. [More Information]
Chan, J., Kuk, A. (1997). Maximum likelihood estimation for probit-linear mixed models with correlated random effects. Biometrics, 53(1), 86-97.
Bell, J., Mattick, R., Hay, A., Chan, J., Hall, W. (1997). Methadone maintenance and drug-related crime. Journal of Substance Abuse Treatment, 9, 15-25. [More Information]
Bell, J., Chan, J., Kuk, A. (1995). Investigating the influence of treatment philosophy on outcome of methadone maintenance. Addiction, 90, 823-830. [More Information]

show 69 more

Conferences

Wong, S., Chan, J., Azizi, L., Xu, R. (2021). Supervised temporal autoencoder for stock return time-series forecasting. IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021, USA: IEEE. [More Information]
Choy, S., Chan, J. (2008). Bayesian analysis of stochastic of volatilities using the generalized-t distribution. Joint Meeting of 4th World Conference of the IASC and 6th Conference of the Asian Regional Section of the IASC on Computational Statistics & Data Analysis, Yokohama.
Chan, J., Lam, C., Chen, C., Choy, S. (2008). Threshold geometric process model for financial time series. Joint Meeting of 4th World Conference of the IASC and 6th Conference of the Asian Regional Section of the IASC on Computational Statistics & Data Analysis, Yokohama.
Choy, S., Chan, J., Yam, C. (2003). Robust analysis of salamander data, Generalized Linear model with random effects. 7th Valencia International Meeting on Bayesian Statistics, New York: Oxford University Press.

show 1 more

Report

Bell, J., Ward, J., Mattick, R., Hay, A., Chan, J., Hall, W. (1995). An evaluation of private methadone clinics.

By Year

Expand all

2025

Wong, S., Chan, J., Azizi, L. (2025). Quantifying neural network uncertainty under volatility clustering. Neurocomputing, 614, 128816 (14 pages). [More Information]

2023

Usman, F., Chan, J., Makov, U., Wang, Y., Dong, A. (2023). Claim Prediction and Premium Pricing for Telematics Auto Insurance Data Using Poisson Regression with Lasso Regularisation. Risks, 12(137), 33 pages. [More Information]
Nitithumbundit, T., Chan, J. (2023). Maximum leave-one-out likelihood method for the location parameter of variance gamma distribution with unbounded density. Journal of Statistical Computation and Simulation, 93(15), 2642-2671. [More Information]
Tan, S., Ng, K., Chan, J. (2023). Predicting Returns, Volatilities and Correlations of Stock Indices Using Multivariate Conditional Autoregressive Range and Return Models. Mathematics, 11 Open Access(1), 13-1-13-24. [More Information]
James, N., Menzies, M., Chan, J. (2023). Semi-Metric Portfolio Optimization: A New Algorithm Reducing Simultaneous Asset Shocks. Econometrics, 11 (Open Access)(1), Article no. 8 - 1-Article no. 8 - 33. [More Information]

show 1 more

2022

Nitithumbundit, T., Chan, J. (2022). Covid-19 impact on Cryptocurrencies market using Multivariate Time Series Models. Quarterly Review of Economics and Finance, 86, 365-375. [More Information]
Peters, G., Yan, H., Chan, J. (2022). Model risk in mortality-linked contingent claims pricing. Journal of Risk Model Validation, 16(3), 1-53. [More Information]
Usman, F., Chan, J. (2022). New loss reserve models with persistence effects to forecast trapezoidal losses in run-off triangles. Astin Bulletin, 52(3), 877-920. [More Information]
Wong, S., Chan, J., Azizi, L., Xu, R. (2022). Time-varying neural network for stock return prediction. Intelligent Systems in Accounting, Finance and Management, 29(1), 3-18. [More Information]
Chan, J., Choy, S., Makov, U., Shamir, A., Shapovalov, V. (2022). Variable Selection Algorithm for a Mixture of Poisson Regression for Handling Overdispersion in Claims Frequency Modeling Using Telematics Car Driving Data. Risks, 10 (Open Access)(4), Article No 83. [More Information]

show 2 more

2021

Peiris, M., Chan, J., Jajo, N. (2021). A Quick Reference Guide to Beginners of Statistics and Data Science Using RStudio. Indonesia: CV. Meugah Printindo.
James, N., Menzies, M., Chan, J. (2021). Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19. Physica A, 565, 125581. [More Information]
Nitithumbundit, T., Chan, J. (2021). ECM algorithm for estimating vector ARMA model with variance gamma distribution and possible unbounded density. Australian & New Zealand Journal of Statistics, 63(3), 485-516. [More Information]
Tan, S., Chan, J., Ng, K. (2021). Modelling and forecasting stock volatility and return: a new approach based on quantile Rogers-Satchell volatility measure with asymmetric bilinear CARR model. Studies in Nonlinear Dynamics and Econometrics, In Press, 1-38. [More Information]
Yan, H., Peters, G., Chan, J. (2021). Mortality models incorporating long memory for life table estimation: A comprehensive analysis. Annals of Actuarial Science, 15(3), 567-604. [More Information]
Peters, G., Yan, H., Chan, J. (2021). Statistical features of persistence and long memory in mortality data. Annals of Actuarial Science, 15(2), 291-317. [More Information]
Wang, J., Chan, J. (2021). Stochastic modelling of volatility and interrelationships in the Australian electricity markets. Communications in Statistics: Simulation and Computation, In Press, 1-21. [More Information]
Wong, S., Chan, J., Azizi, L., Xu, R. (2021). Supervised temporal autoencoder for stock return time-series forecasting. IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021, USA: IEEE. [More Information]

show 5 more

2020

Nitithumbundit, T., Chan, J. (2020). ECM Algorithm for Auto-Regressive Multivariate Skewed Variance Gamma Model with Unbounded Density. Methodology and Computing in Applied Probability, 22(3), 1169-1191. [More Information]
Yan, H., Peters, G., Chan, J. (2020). Multivariate Long-Memory Cohort Mortality Models. Astin Bulletin, 50(1), 223-263. [More Information]
James, N., Menzies, M., Azizi, L., Chan, J. (2020). Novel semi-metrics for multivariate change point analysis and anomaly detection. Physica D: Nonlinear Phenomena, 412(November 2020), 132636 - 1-132636 - 15. [More Information]
Phillip, A., Chan, J., Peiris, M. (2020). On generalized bivariate student-t Gegenbauer long memory stochastic volatility models with leverage: Bayesian forecasting of cryptocurrencies with a focus on Bitcoin. Econometrics and Statistics, 16, 69-90. [More Information]
Chan, J., Choy, S., Walker, S. (2020). On the estimation of the shape parameter of a symmetric distribution. Journal of Data Science, 18(1), 78-100. [More Information]
Tan, S., Chan, J., Ng, K. (2020). On the speculative nature of cryptocurrencies: A study on Garman and Klass volatility measure. Finance Research Letters, 32, 101075. [More Information]

show 3 more

2019

Chan, J., Ng, K., Ragell, R. (2019). Bayesian return forecasts using realised range and asymmetric CARR model with various distribution assumptions. International Review of Economics and Finance, 61, 188-212. [More Information]
Chan, J., Ng, K., Nitithumbundit, T., Peiris, M. (2019). Efficient estimation of financial risk by regressing the quantiles of parametric distributions: An application to CARR models. Studies in Nonlinear Dynamics and Econometrics, 23(2), 1-22. [More Information]
Yatigammana, R., Chan, J., Gerlach, R. (2019). Forecasting trade durations via ACD models with mixture distributions. Quantitative Finance, 19(12), 2051-2067. [More Information]
Phillip, A., Chan, J., Peiris, M. (2019). On long memory effects in the volatility measure of Cryptocurrencies. Finance Research Letters, 28, 95-100. [More Information]
Tan, S., Ng, K., Chan, J., Mohamed, I. (2019). Quantile range-based volatility measure for modelling and forecasting volatility using high frequency data. North American Journal of Economics and Finance, 47, 537-551. [More Information]

show 2 more

2018

Phillip, A., Chan, J., Peiris, M. (2018). A new look at Cryptocurrencies. Economics Letters, 163, 6-9. [More Information]
Phillip, A., Chan, J., Peiris, M. (2018). Bayesian estimation of Gegenbauer long memory processes with stochastic volatility: methods and applications. Studies in Nonlinear Dynamics and Econometrics, 22(3), 1-29. [More Information]
Chan, J., Choy, S., Makov, U., Landsman, Z. (2018). Modelling Insurance Losses Using Contaminated Generalised Beta Type-II Distribution. Astin Bulletin, 48(2), 871-904. [More Information]

2017

Ng, K., Peiris, M., Chan, J., Allen, D., Ng, K. (2017). Efficient modelling and forecasting with range based volatility models and its application. North American Journal of Economics and Finance, 42, 448-460. [More Information]

2016

Yatigammana, R., Choy, S., Chan, J. (2016). Autoregressive Conditional Duration Model with an Extended Weibull Error Distribution. In Van-Nam Huynh, Vladik Kreinovich, Songsak Sriboonchitta (Eds.), Causal Inference in Econometrics, (pp. 83-107). Cham: Springer. [More Information]
Chan, J., Wan, W. (2016). Bayesian analysis of Cannabis offences using generalized Poisson geometric process model with flexible dispersion. Journal of Statistical Computation and Simulation, 86(16), 3315-3336. [More Information]
Chan, J. (2016). Bayesian informative dropout model for longitudinal binary data with random effects using conditional and joint modeling approaches. Biometrical Journal, 58(3), 549-569. [More Information]
Choy, S., Chan, J., Makov, U. (2016). Robust Bayesian analysis of loss reserving data using scale mixtures distributions. Journal of Applied Statistics, 43(3), 396-411. [More Information]

show 1 more

2015

Chan, J. (2015). Predicting loss reserves using quantile regression Running title: Quantile regression loss reserve models. Journal of Data Science, 13(1), 127-156.
Dong, A., Chan, J., Peters, G. (2015). Risk margin quantile function via parametric and non-parametric Bayesian approaches. Astin Bulletin, 45(3), 503-550. [More Information]

2014

Chan, J., Wan, W., Yu, P. (2014). A Poisson geometric process approach for predicting drop-out and committed first-time blood donors. Journal of Applied Statistics, 41(7), 1486-1503. [More Information]
Chan, J., Lam, C., Choy, S. (2014). An Innovative Financial Time Series Model: The Geometric Process Model. In Van-Nam Huynh, Vladik Kreinovich, Songsak Sriboonchitta (Eds.), Modeling Dependence in Econometrics, (pp. 81-99). Cham: Springer. [More Information]
Chan, J. (2014). Analysis of Correlation Structures using Generalized Estimating Equation Approach for Longitudinal Binary Data. Journal of Data Science, 12, 293-305.
Chan, J., Choy, S., Lam, C. (2014). Modeling Electricity Price Using A Threshold Conditional Autoregressive Geometric Process Jump Model. Communications in Statistics - Theory and Methods, 43(10-12), 2505-2515. [More Information]
Chan, J., Wan, W. (2014). Multivariate generalized Poisson geometric process model with scale mixtures of normal distributions. Journal of Multivariate Analysis, 127, 72-87. [More Information]

show 2 more

2013

Dong, X., Chan, J. (2013). Bayesian analysis of loss reserving using dynamic models with generalized beta distribution. Insurance: Mathematics and Economics, 53, 355-365. [More Information]
Rosner, B., Peiris, M., Chan, J., Marchev, D. (2013). MATH1015: Biostatistics. Sydney: Cengage Learning.
Wang, J., Choy, S., Chan, J. (2013). Modelling Stochastic Volatility using Generalized t distribution. Journal of Statistical Computation and Simulation, 83(2), 340-354. [More Information]
Jones, C., Kemp, R., Chan, J. (2013). The relationship between delay discounting, judicial supervision, and substance use among adult drug court clients. Psychology, Public Policy, and Law, 19(4), 454-465. [More Information]

show 1 more

2012

Chan, J., Lam, C., Yu, P., Choy, S., Chen, C. (2012). A Bayesian conditional autoregressive geometric process model for range data. Computational Statistics and Data Analysis, 56(11), 3006-3019. [More Information]

2011

Chen, C., Chan, J., Gerlach, R., Hsieh, W. (2011). A comparison of estimators for regression models with change points. Statistics and Computing, 21(3), 395-414. [More Information]
Wan, W., Chan, J. (2011). Bayesian analysis of robust Poisson geometric process model using heavy-tailed distributions. Computational Statistics and Data Analysis, 55(1), 687-702. [More Information]
Chan, J., Wan, W. (2011). Bayesian approach to analysing longitudinal bivariate binary data with informative dropout. Computational Statistics, 26(1), 121-144. [More Information]
Chen, C., Chan, J., So, M., Lee, K. (2011). Classification in segmented regression problems. Computational Statistics and Data Analysis, 55(7), 2276-2287. [More Information]
Wang, J., Chan, J., Choy, S. (2011). Stochastic Volatility Models with Leverage and Heavy-Tailed Distributions: A Bayesian Approach Using Scale Mixtures. Computational Statistics and Data Analysis, 55(1), 852-862. [More Information]

show 2 more

2010

Chan, J., Leung, D. (2010). Binary geometric process model for the modeling of longitudinal binary data with trend. Computational Statistics, 25, 505-536. [More Information]

2009

Wan, W., Chan, J. (2009). A New Approach for Handling Longitudinal Count Data with Zero-Inflation and Overdispersion: Poisson Geometric Process Model. Biometrical Journal, 51(4), 556-570. [More Information]
Choy, S., Chan, J., Makov, U. (2009). Model selection for loss reserves: The growing triangle technique. Risk, Life and Pensions, 5, 35-40.
Chan, J., Leung, D., Choy, S., Wan, W. (2009). Nonignorable dropout models for longitudinal binary data with random effects: An application of Monte Carlo approximation through the Gibbs output. Computational Statistics and Data Analysis, 53(12), 4530-4545. [More Information]

2008

Chan, J., Choy, S. (2008). Analysis of covariance structures in time series. Journal of Data Science, 6(4), 573-589.
Chan, J., Choy, S. (2008). Analysis of covariance structures in time series. Journal of Data Science, 6(4), 573-589.
Choy, S., Chan, J. (2008). Bayesian analysis of stochastic of volatilities using the generalized-t distribution. Joint Meeting of 4th World Conference of the IASC and 6th Conference of the Asian Regional Section of the IASC on Computational Statistics & Data Analysis, Yokohama.
Chan, J., Choy, S., Makov, U. (2008). Robust Bayesian Analysis of Loss Reserves Data using the Generalized-t Distribution. Astin Bulletin, 38(1), 207-230. [More Information]
Choy, S., Chan, J. (2008). Scale mixtures distributions in statistical modelling. Australian and New Zealand Journal of Statistics, 50(2), 135-146. [More Information]
Chan, J., Lam, C., Chen, C., Choy, S. (2008). Threshold geometric process model for financial time series. Joint Meeting of 4th World Conference of the IASC and 6th Conference of the Asian Regional Section of the IASC on Computational Statistics & Data Analysis, Yokohama.

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2007

Chan, J., Choy, S., Lee, A. (2007). Bayesian analysis of constant elasticity of variance models. Applied Stochastic Models in Business and Industry, 23(1), 83-96. [More Information]
Yu, P., Chung, K., Lin, C., Chan, J., Lee, C. (2007). Predicting potential drop-out and future commitment for first time donors based on first 1.5-year donation patterns: the case in Hong Kong Chinese Donors. Vox Sanguinis, 93(1), 57-63. [More Information]

2006

Chan, J., Yu, P., Lam, Y., Ho, A. (2006). Modelling SARS data using threshold geometric process. Statistics in Medicine, 25(11), 1826-1839. [More Information]
Yu, P., Chan, J., Fung, W. (2006). Statistical Exploration from SARS. The American Statistician, 60(1), 81-91. [More Information]

2005

Chan, J., Kuk, A., Yam, C. (2005). Monte Carlo Approximation through Gibbs output in Generalized linear mixed models. Journal of Multivariate Analysis, 94(2), 300-312. [More Information]

2004

Lam, Y., Zhu, L., Chan, J., Liu, Q. (2004). Analysis of Data from a Series of Events by a Geometric Process Model. Acta Mathematicae Applicatae Sinica, 20(2), 263-282. [More Information]
Chan, J., Yeh, L., Leung, D. (2004). Statistical inference for geometric processes with gamma distributions. Computational Statistics and Data Analysis, 47(3), 565-581. [More Information]

2003

Choy, S., Chan, J., Yam, C. (2003). Robust analysis of salamander data, Generalized Linear model with random effects. 7th Valencia International Meeting on Bayesian Statistics, New York: Oxford University Press.

2001

Kuk, A., Chan, J. (2001). Three ways of implementing the EM algorithm when parameters are not identifiable. Biometrical Journal, 43(2), 207-218.

2000

Chan, J. (2000). Initial Stage Problem in Autoregressive Binary Regression. Journal of the Royal Statistical Society: Series D (The Statistician), 49(4), 495-502. [More Information]

1998

Lam, Y., Chan, J. (1998). Statistical inference for geometric processes with lognormal distribution. Computational Statistics and Data Analysis, 27(1), 99-112. [More Information]
Chan, J., Kuk, A., Bell, J., Gilchrist, C. (1998). The Analysis of Methadone Clinic Data Using Marginal and Conditional Logistic Models with Mixture or Random Effects. Australian & New Zealand Journal of Statistics, 40(1), 1-10. [More Information]

1997

Chan, J., Kuk, A., Bell, J. (1997). A likelihood approach to analysing longitudinal bivariate binary data. Biometrical Journal, 39(4), 409-421. [More Information]
Chan, J., Kuk, A. (1997). Maximum likelihood estimation for probit-linear mixed models with correlated random effects. Biometrics, 53(1), 86-97.
Bell, J., Mattick, R., Hay, A., Chan, J., Hall, W. (1997). Methadone maintenance and drug-related crime. Journal of Substance Abuse Treatment, 9, 15-25. [More Information]

1995

Bell, J., Ward, J., Mattick, R., Hay, A., Chan, J., Hall, W. (1995). An evaluation of private methadone clinics.
Bell, J., Chan, J., Kuk, A. (1995). Investigating the influence of treatment philosophy on outcome of methadone maintenance. Addiction, 90, 823-830. [More Information]

Selected Grants

2003

Modeling of SARS data using threshold Geometric Process models, Chan J, The University of Hong Kong/Committee on Research and Conference Grants
New Methodologies for Loss Reserves and Other Aspects in Insurance Industry, Chan J, The University of Hong Kong/University Research Committee

2002

Generalized Geometric process with application, Chan J, The University of Hong Kong/University Research Committee

2001

Likelihood and Bayesian analysis of stochastic volatility, jump diffusion and other financial models, Chan J, The University of Hong Kong/Seed Funding for Basic Research

1999

Extension of informative drop-out models to handle multiple responses and random effects in longitud, Chan J, The University of Hong Kong/Committee on Research and Conference Grants

Faculty of Science

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Professor Jennifer Chan

Timetable

Selected publications

Publications

By Type

By Year

Selected Grants

2003

2002

2001

1999