Publications

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  • B. Hines, Yuriy Kuleshov, G. Qian. Spatial Modelling of Linear Regression Coefficients for Gauge Measurements Against Satellite Estimates. 4, 217-234, Springer International Publishing, 2021. doi: 10.1007/978-3-030-62497-2_11.

  • Hong Wang, G. Qian, A. TORDESILLAS. Modeling big spatio-temporal geo-hazards data for forecasting by error-correction cointegration and dimension-reduction. Spatial Statistics, 36, 100432 (24pp), 2020. doi: 10.1016/j.spasta.2020.100432.

  • Guangbao Guo, G. Qian, Lu Lin, Wei Shao. Parallel inference for big data with the group Bayesian method. Metrika, 225-243, 2020. doi: 10.1007/s00184-020-00784-0.

  • S Feng, J Li, G. Qian. Association between the yield and the main agronomic traits of Tartary buckwheat evaluated using the random forest model. Crop Science, 60, 2394-2407, 2020. doi: 10.1002/csc2.20243.

  • George Xianzhi Yuan, Lan Di, Yudi Gu, G. Qian. The Prediction for the Outbreak of COVID-19 for 15 States in USA by Using Turning Phase Concepts As of April 10, 2020. 2020. doi: 10.2139/ssrn.3575002.

  • George Xianzhi Yuan, Lan Di, Yudi Gu, G. Qian. The Prediction for the Outbreak of COVID-19 in European Countries by Using Turning Phase Concepts as of April 9, 2020. 2020. doi: 10.2139/ssrn.3574989.

  • George Xianzhi Yuan, Lan Di, Yudi Gu, G. Qian. The Prediction for the Outbreak of COVID-19 for 15 States in USA by Using Turning Phase Concepts as of April 10, 2020. 2020. doi: 10.1101/2020.04.13.20064048.

  • George Xianzhi Yuan, Lan Di, Yudi Gu, G. Qian. The Framework for the Prediction of the Critical Turning Period for Outbreak of COVID-19 Spread in China based on the iSEIR Model1. 2020. doi: 10.1101/2020.04.05.20054346.

  • George Xianzhi Yuan, Lan Di, Yudi Gu, G. Qian. The Framework for the Prediction of the Critical Turning Period for Outbreak of COVID-19 Spread in China based on the iSEIR Model. 2020.

  • G. Qian, Y Wu, M Xu. Multiple change-points detection by empirical Bayesian information criteria and Gibbs sampling induced stochastic search. Applied Mathematical Modelling, 72, 202-216, 2019. doi: 10.1016/j.apm.2019.03.012.

  • Chun Fung Kwok, G. Qian. Missing data analysis, combinatorial model selection and structure learning. 2019.

  • Pu Xue Qiao, G. Qian. Copula-based spatio-temporal modelling for count data. 2019.

  • Zhendong Huang, Davide Ferrari, D. Ferrari, G. Qian. Parsimonious and powerful composite likelihood testing for group difference and genotype-phenotype association. Computational Statistics & Data Analysis, 110, 37-49, 2017. doi: 10.1016/j.csda.2016.12.004.

  • Zemei Xu, Daniel F Schmidt, Enes Makalic, G. Qian, John L Hopper. Bayesian Sparse Global-Local Shrinkage Regression for Selection of Grouped Variables. 2017.

  • Jasper S Wijnands, G. Qian, Yuriy Kuleshov. Variable Selection for Tropical Cyclogenesis Predictive Modeling. Monthly Weather Review, 144, 4605-4619, 2016. doi: 10.1175/MWR-D-16-0166.1.

  • Robert J MacInnis, Daniel F Schmidt, Enes Makalic, Gianluca Severi, Liesel M FitzGerald, Matthias Reumann, Miroslaw K Kapuscinski, Adam Kowalczyk, Zeyu Zhou, Benjamin Goudey, G. Qian, Quang M Bui, Daniel J Park, Adam Freeman, Melissa C Southey, Ali Amin Al Olama, Zsofia Kote-Jarai, Rosalind A Eeles, John L Hopper, Graham G Giles, AAA Olama, UK Genetic Prostate Cancer Study Collaborators, A Amin Al Olama. Use of a Novel Nonparametric Version of DEPTH to Identify Genomic Regions Associated with Prostate Cancer Risk. Cancer Epidemiology, Biomarkers & Prevention, 25, 1619-1624, 2016. doi: 10.1158/1055-9965.EPI-16-0301.

  • Jasper S Wijnands, G. Qian, Yuriy Kuleshov, J Wijnands. Spline-based modelling of near-surface wind speeds in tropical cyclones. Applied Mathematical Modelling, 40, 8685-8707, 2016. doi: 10.1016/j.apm.2016.05.013.

  • G. Qian, Yuehua Wu, Davide Ferrari, D. Ferrari, Puxue Qiao, Frederic Hollande. Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications. Computational Intelligence and Neuroscience, 2016, 4037380-13, 2016. doi: 10.1155/2016/4037380.

  • Guangbao Guo, Wenjie You, Lu Lin, G. Qian. Covariance matrix and transfer function of dynamic generalized linear models. Journal of Computational and Applied Mathematics, 296, 613-624, 2016. doi: 10.1016/j.cam.2015.10.015.

  • G. Qian, Calyampudi Radhakrishna Rao, Xiaoying Sun, Yuehua Wu. Boosting association rule mining in large datasets via Gibbs sampling. Proceedings of the National Academy of Sciences of the United States of America, 113, 4958-4963, 2016. doi: 10.1073/pnas.1604553113.

  • Davide Ferrari, D. Ferrari, G. Qian, Tane Hunter, D Ferrari. Parsimonious and Efficient Likelihood Composition by Gibbs Sampling. Journal of Computational and Graphical Statistics, 25, 935-953, 2016. doi: 10.1080/10618600.2015.1058799.

  • J. Wijnands, G. Qian. Tropical cyclones: improving forecast accuracy in Australia and the South Pacific Ocean. 2016.

  • Z. Xu, DF Schmidt, E Makalic, G. Qian, J. Hopper. Bayesian grouped horseshoe regression with application to additive models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9992 LNAI, 229-240, 2016. doi: 10.1007/978-3-319-50127-7_19.

  • Ling Ding, G. Qian. Regression clustering using Gibbs sampler and optimal cluster number estimation. 2016.

  • Zeyu Zhou, G. Qian. Statistical testing and selection by re-sampling in genome- wide association studies. 2016.

  • JS Wijnands, G. Qian, KL Shelton, RJB Fawcett, JCL Chan, Y Kuleshov. Seasonal forecasting of tropical cyclone activity in the Australian and the South Pacific Ocean regions. Mathematics of Climate and Weather Forecasting, 1, 2015. doi: 10.1515/mcwf-2015-0002.

  • Guangbao Guo, Wenjie You, G. Qian, Wei Shao. Parallel maximum likelihood estimator for multiple linear regression models. Journal of Computational and Applied Mathematics, 273, 251-263, 2015. doi: 10.1016/j.cam.2014.06.005.

  • Zhao, G. Qian. On Multivariate Time Series Model Selection Involving Many Candidate VAR Models. European Journal of Pure and Applied Mathematics, 7, 1-21, 2014.

  • G. Qian, Shi, Wu. A Statistical Test of Change-Point in Mean That Almost Surely Has Zero Error Probabilities. Australian & New Zealand Journal of Statistics, 55, 435-454, 2013. doi: 10.1111/anzs.12049.

  • Alan Simpson, G. Qian. On some model comparison problems. 2013.

  • M Reumann, E Makalic, B Goudey, MI Inouye, A Bickerstaffe, Q. Bui, DJ Park, MK Kapuscinski, DF Schmidt, Z. Zhou, G. Qian, J Zobel, J. Wagner, J. Hopper. Supercomputing enabling exhaustive statistical analysis of genome wide association study data: Preliminary results. Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012, 1258-1261, 2012. doi: 10.1109/EMBC.2012.6346166.

  • G. Qian, N. Li, R. Huggins. Using capture-recapture data and hybrid Monte Carlo sampling to estimate an animal population affected by an environmental catastrophe. Computational Statistics & Data Analysis, 55, 655-666, 2011. doi: 10.1016/j.csda.2010.06.009.

  • G. Qian. Use Computer Algebra System to Facilitate Teaching and Learning of Probability in Business and Science Schools. Istanbul Universitesi Isletme Fakultesi Dergisi, 40, 72-85, 2011.

  • G. Qian. Teaching, Learning and Retention of Statistical Ideas in Introductory Statistical Education. European Journal of Pure and Applied Mathematics, 4, 103-116, 2011.

  • G. Qian, Wu. Estimation and Selection in Regression Clustering. European Journal of Pure and Applied Mathematics, 4, 455-466, 2011.

  • G. Qian. Stochastic complexity, histograms and hypothesis testing of homogeneity. European Journal of Pure and Applied Mathematics, 3, 51-80, 2010.

  • LLH Andrew, G. Qian, F. Vazquez-Abad, FJ Vázquez-Abad. Setwise and filtered Gibbs samplers for teletraffic analysis. ACM Transactions on Modeling and Computer Simulation, 20, 1-24, 2010. doi: 10.1145/1734222.1734223.

  • G. Qian. Law of iterated logarithm and strong consistency in Poisson regression model selection. European Journal of Pure and Applied Mathematics, 3, 417-434, 2010.

  • J Cui, D Pitt, G. Qian. Model Selection and Claim Frequency for Workers' Compensation Insurance. Astin Bulletin, 40, 779-796, 2010. doi: 10.2143/AST.40.2.2061136.

  • G. Qian, Wu, Shao. A procedure for estimating the number of clusters in logistic regression clustering. Journal of Classification, 26, 183-199, 2009. doi: 10.1007/s00357-009-9035-y.

  • G. Qian, Rao, Wu, Shao. Estimating the number of clusters in logistic regression clustering by an information theoretic criterion. 29-44, Springer, 2008.

  • Zhao, G. Qian. Vector Autoregressive Model Selection Based on Gibbs Sampler. Statistical Research, 25, 86-92, 2008.

  • Park, G. Qian, Jun. Monte Carlo EM algorithm in logistic linear models involving non-ignorable missing data. Applied Mathematics and Computation, 197, 440-450, 2008. doi: 10.1016/j.amc.2007.07.080.

  • G. Qian, X. Zhao. On time series model selection involving many candidate ARMA models. Computational Statistics & Data Analysis, 51, 6180-6196, 2007. doi: 10.1016/j.csda.2006.12.044.

  • J Cui, G. Qian. Selection of Working Correlation Structure and Best Model in GEE Analyses of Longitudinal Data. Communications in Statistics - Simulation and Computation, 36, 987-996, 2007. doi: 10.1080/03610910701539617.

  • N. Li, G. Qian, R. Huggins. A latent variable model for estimating disease transmission rate from data on household outbreaks. Computational Statistics & Data Analysis, 50, 3354-3368, 2006. doi: 10.1016/j.csda.2005.06.011.

  • G. Qian, Y. Wu. Strong limit theorems on model selection in generalized linear regression with binomial responses. Statistica Sinica, 16, 1335-1365, 2006.

  • N. Li, G. Qian. Modelling disease transmission by the method of data augmentation. Statistical Solutions to Modern Problems, Proceedings of the 20th International Workshop on Statistical Modelling, Sydney, 329-336, 2005.

  • R. Huggins, Loesch, G. Qian, Q. Bui, Mitchell, Dobson, Taylor. Hierarchical Bayes model for random haplotype and family effects in the transmission of fragile-X. Genetic Epidemiology, 26, 294-304, 2004. doi: 10.1002/gepi.10316.

  • G. Qian, R. Huggins, Loesch. Application of the Rasch model in categorical pedigree analysis using MCEM: I Binary data. Discussiones Mathematicae, 24, 255-280, 2004.

  • N. Li, G. Qian, R. Huggins. A random effects model for diseases with heterogeneous rates of infection. Journal of Statistical Planning and Inference, 116, 317-332, 2003. doi: 10.1016/S0378-3758(02)00232-X.

  • N. Li, G. Qian, R. Huggins. Analyses of between-household heterogeneity in disease transmission from data on outbreak sizes. Australian & New Zealand Journal of Statistics, 44, 401-411, 2002. doi: 10.1111/1467-842X.00242.

  • G. Qian, Field. Law of iterated logarithm and consistent model selection criterion in logistic regression. Statistics and Probability Letters, 56, 101-112, 2002. doi: 10.1016/S0167-7152(01)00191-2.

  • G. Qian, Field. Using MCMC for logistic regression model selection involving large number of candidate models. Monte Carlo and Quasi-Monte Carlo Methods 2000, 460-474, 2000.

  • R. Huggins, G. Qian, Loesch. Inference on random coefficient models for haplotype effects in dynamic mutations using MCMC. Selected Proceedings of the Symposium on Inference for Stochastic Processes, 37, 185-202, 2000.

  • MC Bueso, G. Qian, JM Angulo. Stochastic complexity and model selection from incomplete data. Journal of Statistical Planning and Inference, 76, 273-284, 1999. doi: 10.1016/S0378-3758(98)00112-8.

  • MC Bueso, JM Angulo, G. Qian, FJ Alonso. Spatial sampling design based on stochastic complexity. Journal of Multivariate Analysis, 71, 94-110, 1999. doi: 10.1006/jmva.1999.1834.

  • G. Qian. Computations and analysis in robust regression model selection using stochastic complexity. Computational Statistics, 14, 293-314, 1999.

  • G. Qian. Computing minimum description length for robust linear regression model selection.. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 314-325, 1999.

  • G. Qian, HR Kunsch, HR Künsch. On model selection via stochastic complexity in robust linear regression. Journal of Statistical Planning and Inference, 75, 91-116, 1998. doi: 10.1016/S0378-3758(98)00138-4.

  • G. Qian, HR Künsch, Guoqi Qian. Some notes on Rissanen's stochastic complexity. IEEE Transactions on Information Theory, 44, 782-786, 1998. doi: 10.1109/18.661521.

  • G. Qian. COMPUTING MINIMUM DESCRIPTION LENGTH FOR ROBUST LINEAR REGRESSION MODEL SELECTION. Biocomputing '99, 314-325, 1998. doi: 10.1142/9789814447300_0031.

  • G. Qian, RP Gupta, G Gabor. Test for homogeneity of several populations by stochastic complexity. Journal of Statistical Planning and Inference, 53, 133-151, 1996. doi: 10.1016/0378-3758(95)00130-1.

  • G. Qian, G Gabor, RP Gupta. Generalised linear model selection by the predictive least quasi-deviance criterion. Biometrika, 83, 41-54, 1996. doi: 10.1093/biomet/83.1.41.

  • G. Qian, G Gabor, RP Gupta. Principal components selection by the criterion of the minimum mean difference of complexity. Journal of Multivariate Analysis, 49, 55-75, 1994. doi: 10.1006/jmva.1994.1013.

  • G. Qian, G Gabor, RP Gupta, Guoqi Qian. On stochastic complexity estimation - a decision-theoretic approach. IEEE Transactions on Information Theory, 40, 1181-1191, 1994. doi: 10.1109/18.335957.