J. Chan

发表

MAXIMUM LIKELIHOOD ESTIMATION FOR PROBIT-LINEAR MIXED MODELS WITH CORRELATED RANDOM EFFECTS

A. Kuk, J. Chan, 1997 .

Analysis of Data from a Series of Events by a Geometric Process Model

Lixing Zhu, J. Chan, Qun Liu, 2004 .

SCALE MIXTURES DISTRIBUTIONS IN STATISTICAL MODELLING

S. T. Boris Choy, J. Chan, 2008 .

A New Approach for Handling Longitudinal Count Data with Zero‐Inflation and Overdispersion: Poisson Geometric Process Model

Jennifer S K Chan, Wai-Yin Wan, J. Chan, 2009, Biometrical journal. Biometrische Zeitschrift.

Maximum leave-one-out likelihood estimation for location parameter of unbounded densities

J. Chan, Thanakorn Nitithumbundit, 2016, 1602.01213.

Modelling stochastic volatility using generalized t distribution

Joanna J. J. Wang, J. Chan, Joanna (Jia Jia) Wang, 2013 .

MODELLING INSURANCE LOSSES USING CONTAMINATED GENERALISED BETA TYPE-II DISTRIBUTION

U. Makov, Z. Landsman, J. Chan, 2018, ASTIN Bulletin.

PROOF COVER SHEET

J. Chan, 2016 .

Bayesian return forecasts using realised range and asymmetric CARR model with various distribution assumptions

J. Chan, K. Ng, Rachel Ragell, 2019, International Review of Economics & Finance.

Forecasting trade durations via ACD models with mixture distributions

J. Chan, R. Gerlach, R. Yatigammana, 2019, Quantitative Finance.

Bayesian analysis of loss reserving using dynamic models with generalized beta distribution

Alice X.D. Dong, Jennifer S. K. Chan, J. Chan, 2013 .

Bayesian estimation of Gegenbauer long memory processes with stochastic volatility: methods and applications

S. Peiris, J. Chan, A. Phillip, 2018 .

Risk Margin Quantile Function via Parametric and Non-Parametric Bayesian Quantile Regression

G. Peters, J. Chan, A. Dong, 2014, 1402.2492.

Modelling and Forecasting Stock Volatility and Return: A New Approach Based on Quantile Rogers-Satchell Volatility Measure With Asymmetric Bilinear CARR Model

J. Chan, Shay-Kee Tan, Kok Haur Ng, 2020 .

Quantile range-based volatility measure for modelling and forecasting volatility using high frequency data

J. Chan, K. Ng, I. Mohamed, 2019, The North American Journal of Economics and Finance.

Efficient estimation of financial risk by regressing the quantiles of parametric distributions: An application to CARR models

S. Peiris, J. Chan, Thanakorn Nitithumbundit, 2018, Studies in Nonlinear Dynamics & Econometrics.

Novel semi-metrics for multivariate change point analysis and anomaly detection

Nick James, Max Menzies, Jennifer Chan, 2019, Physica D: Nonlinear Phenomena.

On the speculative nature of cryptocurrencies: A study on Garman and Klass volatility measure

J. Chan, Shay-Kee Tan, Kok Haur Ng, 2020 .

On the Estimation of the Shape Parameter of a Symmetric Distribution

S. Walker, J. Chan, Boris Choy, 2018, Journal of Data Science.

REGRESSION MODELS FOR CORRELATED BINARY DATA

J. Chan, 2018 .

Covid-19 impact on Cryptocurrencies market using Multivariate Time Series Models

J. Chan, Thanakorn Nitithumbundit, 2022, The Quarterly Review of Economics and Finance.

Modeling Electricity Price Using A Threshold Conditional Autoregressive Geometric Process Jump Model

Connie P. Y. Lam, J. Chan, S. Choy, 2014 .

NEW LOSS RESERVE MODELS WITH PERSISTENCE EFFECTS TO FORECAST TRAPEZOIDAL LOSSES IN RUN-OFF TRIANGLES

J. Chan, F. Usman, 2022, ASTIN Bulletin.

Bayesian analysis of Cannabis offences using generalized Poisson geometric process model with flexible dispersion

J. Chan, Wai-Yin Wan, 2016 .

Monte Carlo approximation through Gibbs output in generalized linear mixed models

Anthony Y. C. Kuk, Jennifer S. K. Chan, A. Kuk, 2005 .

Time-varying neural network for stock return prediction

Steven Y. K. Wong, J. Chan, R. Xu, 2020, Intell. Syst. Account. Finance Manag..

RISK MARGIN QUANTILE FUNCTION VIA PARAMETRIC AND NON-PARAMETRIC BAYESIAN APPROACHES

G. Peters, J. Chan, A. Dong, 2015, ASTIN Bulletin.

Bayesian informative dropout model for longitudinal binary data with random effects using conditional and joint modeling approaches

Jennifer S K Chan, J. Chan, 2016, Biometrical journal. Biometrische Zeitschrift.

A Bayesian conditional autoregressive geometric process model for range data

Cathy W. S. Chen, Philip L. H. Yu, S. T. Boris Choy, 2012, Comput. Stat. Data Anal..

Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19

Nick James, Max Menzies, Jennifer Chan, 2020, Physica A: Statistical Mechanics and its Applications.

Mortality models incorporating long memory for life table estimation: a comprehensive analysis

Hongxuan Yan, Gareth W. Peters, Jennifer Chan, 2021, Annals of Actuarial Science.

MULTIVARIATE LONG-MEMORY COHORT MORTALITY MODELS

G. Peters, J. Chan, Hongxuan Yan, 2019, ASTIN Bulletin.

Multivariate Long Memory Cohort Mortality Models

G. Peters, J. Chan, Hongxuan Yan, 2018 .

A Poisson geometric process approach for predicting drop-out and committed first-time blood donors

J. Chan, P. Yu, Wai-Yin Wan, 2014 .

An Innovative Financial Time Series Model: The Geometric Process Model

S. T. Boris Choy, Jennifer S. K. Chan, Connie P. Y. Lam, 2014, TES.

Binary geometric process model for the modeling of longitudinal binary data with trend

Jennifer S. K. Chan, Doris Y. P. Leung, J. Chan, 2010, Comput. Stat..

Modelling SARS data using threshold geometric process

Yeh Lam, Jennifer S K Chan, Philip L H Yu, 2006, Statistics in medicine.

Classification in segmented regression problems

Cathy W. S. Chen, Mike K. P. So, Jennifer S. K. Chan, 2011, Comput. Stat. Data Anal..

Multivariate generalized Poisson geometric process model with scale mixtures of normal distributions

Jennifer So-Kuen Chan, Wai-Yin Wan, J. Chan, 2014, J. Multivar. Anal..

ECM algorithm for estimating vector ARMA model with variance gamma distribution and possible unbounded density

J. Chan, Thanakorn Nitithumbundit, 2020, SSRN Electronic Journal.

Stochastic Modelling of Volatility and Inter-relationships in the Australian Electricity Markets

Joanna J. J. Wang, J. Chan, 2020, Communications in Statistics - Simulation and Computation.

On generalized bivariate student-t Gegenbauer long memory stochastic volatility models with leverage: Bayesian forecasting of cryptocurrencies with a focus on Bitcoin

Shelton Peiris, Andrew Phillip, Jennifer S. K. Chan, 2018 .

Stochastic volatility models with leverage and heavy-tailed distributions: A Bayesian approach using scale mixtures

S. T. Boris Choy, Jennifer S. K. Chan, Joanna J. J. Wang, 2011, Comput. Stat. Data Anal..

Semi-Metric Portfolio Optimization: A New Algorithm Reducing Simultaneous Asset Shocks

J. Chan, Nick James, Max Menzies, 2020, Econometrics.

A new look at Cryptocurrencies

Shelton Peiris, Andrew Phillip, Jennifer S. K. Chan, 2018 .

Supervised Temporal Autoencoder for Stock Return Time-series Forecasting

Jennifer S. K. Chan, Lamiae Azizi, Steven Y. K. Wong, 2021, 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC).

Non-stationary neural network for stock return prediction pdf

Jennifer Chan, Lamiae Azizi, Steven Y. K. Wong, 2020, ArXiv.

Bayesian analysis of robust Poisson geometric process model using heavy-tailed distributions

Jennifer So-Kuen Chan, Wai-Yin Wan, J. Chan, 2010, Computational Statistics & Data Analysis.

Statistical Exploration from SARS

J. Chan, P. Yu, W. Fung, 2006 .

Long Memory Models for Financial Time Series of Counts and Evidence of Systematic Market Participant Trading Behaviour Patterns in Futures on US Treasuries

G. Peters, J. Chan, Hongxuan Yan, 2017 .

Analysis of Covariance Structures in Time Series

Jennifer S. K. Chan, S. T. Boris Choy, J. Chan, 2008, Journal of Data Science.

A comparison of estimators for regression models with change points

Cathy W. S. Chen, Richard Gerlach, Jennifer S. K. Chan, 2010, Stat. Comput..

Analysis of Correlation Structures using Generalized Estimating Equation Approach for Longitudinal Binary Data

Jennifer S. K. Chan, J. Chan, 2021 .

Predicting loss reserves using quantile regression Running title: Quantile regression loss reserve models

J. Chan, 2015, Journal of Data Science.

Statistical features of persistence and long memory in mortality data

G. Peters, J. Chan, Hongxuan Yan, 2021, Annals of Actuarial Science.

Evidence for Persistence and Long Memory Features in Mortality Data

Hongxuan Yan, Gareth Peters, Jennifer Chan, 2019, SSRN Electronic Journal.

Market Maker Inventories and Stock

T. Hendershott, J. Chan, Mark S. Seasholes, 2006 .