Dr Jennifer Chan

F07 - Carslaw Building
The University of Sydney

Telephone 9351 4873
Fax 9351 4534

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, application for insurance data.

I am a member of the Statistics Research group.

Teaching and supervision

Timetable

Selected grants

2004

  • 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.

2003

  • Generalized Geometric process with application; Chan J; The University of Hong Kong/University Research Committee.

2002

  • 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.

2000

  • 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.

Selected publications

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Books

  • Rosner, B., Peiris, M., Chan, J., Marchev, D. (2013). MATH1015: Biostatistics. Sydney: Cengage Learning.

Book Chapters

  • 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.

Journals

  • 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., 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. 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., Makov, U. (2008). Robust Bayesian Analysis of Loss Reserves Data using the Generalized-t Distribution. Astin Bulletin, 38(1), 207-230.
  • Choy, S., Chan, J. (2008). Scale mixtures distributions in statistical modelling. Australian and New Zealand Journal of Statistics, 50(2), 135-146.
  • 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.
  • 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.
  • 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.

Conferences

  • 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.

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.
  • 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]

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]

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]

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. 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.
  • 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.
  • Choy, S., Chan, J. (2008). Scale mixtures distributions in statistical modelling. Australian and New Zealand Journal of Statistics, 50(2), 135-146.
  • 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.

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.

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.

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.

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