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Books/Research Monographs
Jianqing Fan and Jianxin Pan (2020). Contemporary Experimental Design, Multivariate Analysis and Data Mining - Festschrift in Honour of Professor Kai-Tai Fang (edited), Springer Nature, Switzerland (386 pages).
Yu Fei and Jianxin Pan (2005). Case-Deletion Diagnostics in Linear Mixed Models, Science Press, Beijing (195 pages).
Jianxin Pan and Kai-Tai Fang (2002). Growth curve models and statistical diagnostics, Springer Series in Statistics, Springer, New York (387 pages with 109 figures).
Papers
K.T. Fang and J. Pan (2023). A Review of Representative Points of Statistical Distributions and Their Applications. Mathematics, 2023, 11, 2930. https://doi.org/10.3390/math11132930
C. Wang, J. Shen, C. Charalambous, J. Pan (2023). Modeling biomarker variability in joint analysis of longitudinal and time-to-event data, Biostatistics, https://doi.org/10.1093/biostatistics/kxad009
L., Xu, K.T. Fang and J. Pan (2023). Limiting behavior of the gap between the largest two representative points of statistical distributions, Communications in Statistics - Theory and Methods, 52:10, 3290-3313, DOI: 10.1080/03610926.2021.1970772
Y. Yang, H. Dai and J. Pan (2023). Block-diagonal precision matrix regularization for ultra-high dimensional data. Computational Statistics & Data Analysis, 179, 107630, DOI: https://doi.org/10.1016/j.csda.2022.107630
C. Ma, C. Wang and J. Pan. (2022). Multistate modeling and structure selection for multitype recurrent events and terminal event data. Biometrical Journal, 1–15, DOI: https://doi.org/10.1002/bimj.202100334
D. Dai, J. Pan and Y. Liang (2022). Regularized estimation of the Mahalanobis distance based on modified Cholesky decomposition, Communications in Statistics: Case Studies, Data Analysis and Applications, DOI: 10.1080/23737484.2022.2107961
H. Wang, B. Abba and J. Pan (2022). Classical and Bayesian estimations of improved Weibull–Weibull distribution for complete and censored failure times data. Applied Stochastic Models in Business and Industry. 1-22, DOI: https://doi.org/10.1002/asmb.2698
Q. Li and J. Pan (2022). Permutation variation and alternative hyper-sphere decomposition. Mathematics, No. 10, Issue 562, DOI: https://doi.org/10.3390/math10040562
J. Yu, T. Nummi and J. Pan (2022). Mixture regression for longitudinal data based on joint mean–covariance model. Journal of Multivariate Analysis, 104956, DOI: https://doi.org/10.1016/j.jmva.2022.104956
C. Peng, Y. Yang, J. Zhou and J. Pan (2022). Latent Gaussian copula models for longitudinal binary data. Journal of Multivariate Analysis, 104940, DOI: https://doi.org/10.1016/j.jmva.2021.104940
L. Luo and J. Pan (2022). Conditional generalized estimating equations of mean-variance-correlation for clustered data. Computational Statistics & Data Analysis, Vol 168, DOI: https://doi.org/10.1016/j.csda.2021.107386
Y. Pan, Y. Fei, M. Ni, T. Nummi and J. Pan (2021). Growth curve mixture models with unknown covariance structures. Journal of Multivariate Analysis, 104904, DOI: https://doi.org/10.1016/j.jmva.2021.104904
S., Cai, J. Zhou and J. Pan (2021). Estimating the sample mean and standard deviation from order statistics and sample size in meta-analysis. Statistical Methods in Medical Research, 30(12): 2701-2719.
C. Ma and J. Pan (2021). Multi-state Analysis of Multi-type Recurrent Event and Failure Time Data with Event Feedbacks in Biomarkers. Scandinavian Journal of Statistics, DOI: 10.1111/sjos.12545
Y. Huang and J. Pan (2021). Penalized joint generalized estimating equations for longitudinal binary data. Biometrical Journal, 64(1), 57-73, DOI: http://doi.org/10.1002/bimj.202000336.
S. Yuan, J. Zhou, J. Pan and J. Shen (2021). Sphericity and Identity Test for High-dimensional Covariance Matrix using Random Matrix Theory. Acta Mathematicae Applicatae Sinica, 37(2), 214-231.
D. Zhang, X. Cui, C. Li, J. Zhao, J. L. Zeng and J. Pan (2021). Regularized estimation of covariance structure through quadratic loss function. Multivariate, Multilinear and Mixed Linear Models. Springer Nature, DOI: https://doi.org/10.1007/978-3-030-75494-5_4
C. Ma, H. Dai and J. Pan (2021). Modeling past event feedback through biomarker dynamics in the multi-state event analysis for cardiovascular disease data. Annals of Applied Statistics, Vol 15, No. 3, 1308-1328, DOI: https://doi.org/10.1214/21-AOAS1445
Y. Yang, J. Zhou and J. Pan (2021). Estimation and Optimal Structure Selection of High- Dimensional Toeplitz Covariance Matrix. Journal of Multivariate Analysis, Vol 184, DOI: https://doi.org/10.1016/j.jmva.2021.104739
S., Yi, Y. Zhou and J. Pan (2021). Optimal designs of mean-covariance models for longitudinal data. Biometrical Journal, 63(5), 1072-1085, DOI: 10.1002/bimj.202000129
Y. Huang and J. Pan (2020). Joint generalized estimating equations for longitudinal binary data. Computational Statistics & Data Analysis, Vol 155, 107110, DOI: 10.1016/j.csda.2020.107110
C. Kou and J. Pan (2020). Variable Selection in Joint Mean and Covariance Models. Recent Developments in Multivariate and Random Matrix Analysis: Festschrift in Honour of Dietrich von Rosen. Holgersson, T. & Singull, M. (eds.). Springer Nature, p. 219-244 Chapter 13, DOI: 10.1007/978-3-030-56773-6-13
Y. Pan, Y. Fei, M. Ni and J. Pan (2020). Growth curves mixture model with serial covariance structure. Scientia Sinica Mathematica, Vol 50, No. 5, 645-666, DOI: 10.1360/N012019-00145
G. Fu, Y. Wu, M. Zong and J. Pan (2020). Hellinger distance-based stable sparse feature selection for high-dimensional class-imbalanced data. BMC Bioinformatics. Vol. 21, No. 1, DOI: 10.1186/s12859-020-3411-3
G. Li, J. Liang, J. Pan, X. Peng and G. Tian (2020). Multivariate statistics and its applications. Scientia Sinica Mathematica, Vol 50, No. 5, 571-584, DOI: 10.1360/SSM-2020-0071
Q. Wang, J. Huang, J. Pan, H. Wang, J. Xu (2020). A novel method for measuring spatial uniformity of irregular boiling bubbles in a direct contact heat exchanger. International Journal of Energy Research, 44(11), 8823-8840, DOI: 10.1002/er.5577
D. Zhang, X. Cui, C. Li and J. Pan (2020). Estimation of covariance matrix with ARMA structure through quadratic loss function. Contemporary Experimental Design, Multivariate Analysis and Data Mining. Fan, J. & Pan, J. (eds.). Chapter 15, Pages 227-239, Switzerland: Springer Nature.
J. Pan, J. Liang and G. Tian (2020). Walking on the Road to the Statistical Pyramid Prof. Kai-Tai Fang’s Contribution to Multivariate Statistics. Contemporary Experimental Design, Multivariate Analysis and Data Mining. Fan, J. & Pan, J. (eds.). Chapter 1, Pages 3-14, Switzerland: Springer Nature.
Q. Liu, J. Pan, C. Berzuini, M. Rutter and H. Guo (2020). Integrative analysis of Mendelian randomization and Bayesian colocalization highlights four genes with putative BMI-mediated causal pathways to diabetes. Scientific Reports, DOI: https://doi.org/10.1038/s41598-020-64493-4
J. Xu, F. Liu, Q. Xiao, J. Huang, Y. Fei, Y. Yang, Y. Zhai, J. Pan and H. Wang (2020). Synergistic effect of flow pattern evolution of dispersed and continuous phases in direct-contact heat transfer process. International Journal of Refrigeration. 112, 201-214, DOI: 10.1016/j.ijrefrig.2019.11.020
X. Cui, Z. Li, J. Zhao, D. Zhang and J. Pan (2019). Covariance Matrix Regularization for Banded Toeplitz Structure via Frobenius-Norm Discrepancy. IWMS 2016 Madeira Springer Proceedings: Matrices, Statistics and Big Data, DOI: 10.1007/978-3-030-17519-1-9
G. Fu, L. Yi and J. Pan (2019). LASSO-based false-positive selection for class-imbalanced data in metabolomics. Journal of Chemometrics, 33(10), DOI: 10.1002/cem.3177
Chen Chen, Jie Zhou and Jianxin Pan (2019). Correlation structure regularization via entropy loss function for high-dimension and low-sample-size data. Communications in Statistics - Simulation and Computation. DOI: 10.1080/03610918.2019.1571607
Guang-Hui Fu, Lun-Zhao Yi and Jianxin Pan (2018). Tuning model parameters in class-imbalanced learning with precision-recall curve. Biometrical Journal. DOI: https://doi.org/10.1002/bimj.201800148
Wang, Z., Shen, X., Zhu, Y. and Pan, J. (2018). A Tighter Set-Membership Filter for Some Nonlinear Dynamic Systems. IEEE Access. 6, 25351-25362, DOI: 10.1109/ACCESS.2018.2830350
Xiao, Q., Gao, Q., Zhang, J., Xu, J., Pan, J. and Wang, H. (2018). Extraction and evolution of bubbles attributes in a two-phase direct contact evaporator. International Journal of Heat and Mass Transfer. 124, 761-768, DOI: 10.1016/j.ijheatmasstransfer.2018.04.002
Dai, H. and Pan, J. (2018). Joint modelling of survival and longitudinal data with informative observation times. Scandinavian Journal of Statistics. 45, 571-589. DOI: 10.1111/sjos.12314
Tapio Nummi, Janne Salonen, Lasse Koskinen and Jianxin Pan (2018). A semiparametric mixture regression model for longitudinal data. Journal of Statistical Theory and Practice. 12, 1, p. 12-22, DOI: 10.1080/15598608.2017.1298062
Liu X., Li Q. and Pan, J. (2018). A deterministic and stochastic model for the system dynamics of tumor-immune responses to chemotherapy. Physica A: Statistical Mechanics and its Applications. 500, 162-176, DOI: 10.1016/j.physa.2018.02.118
Xiao, Q., Pan, J., Fan, Y., Xu, J. and Wang, H. (2018). An original technique for quantifying the flow-field characteristics in an electrodeposition process of Zn-SiO2 with Fe. Journal of Alloys and Compounds. 737, 448-455, DOI: 10.1016/j.jallcom.2017.12.098
Jianxin Pan and Yi Pan (2017). jmcm: An R Package for Joint Mean-Covariance Modelling of Longitudinal Data. Journal of Statistical Software. DOI: 10.18637/jss.v082.i09
Chao Huang, Daniel Farewell and Jianxin Pan (2017). A calibration method for non-positive definite covariance matrix in multivariate data analysis. Journal of Multivariate Analysis. 157, p.45-52, DOI: 10.1016/j.jmva.2017.03.001
Chuoxin Ma, Maizai Tian and Jianxin Pan (2017). Semiparametric Hierarchical Model with Heteroscedasticity. Statistics and its Interface. 10, 413-424, DOI: dx.doi.org/10.4310/SII.2017.v10.n3.a6
Enbin Song, Qingjiang Shi, Yunmin Zhu, and Jianxin Pan (2017). Robust Hypothesis Testing for Asymmetric Nominal Densities under a Relative Entropy Tolerance. Science China A: Math- ematics. DOI: 10.1007/s11425-000-0000-0.
Xiao, Q., Zhai, Y., Lv, Z., Xu, J., Pan, J. and Wang, H. (2017). Non-uniformity quantification of temperature and concentration fields by statistical measure and image analysis. Applied Thermal Engineering. 124, p.1134-1141, DOI: 10.1016/j.applthermaleng.2017.06.073
Xiao, Q., Pan, J., Lv, Z., Xu, J. and Wang, H. (2017). Measure of bubble non-uniformity within circular region in a direct-contact heat exchanger. International Journal of Heat and Mass Transfer. 110, p.257-261, DOI: 10.1016/j.ijheatmasstransfer.2017.03.042
Xiao, Q., Huang, J., Pan, J., Liu, Y., Xu, J. and Wang, H. (2017). Assessing mixing uniformity of bubbles in direct-contact boiling heat transfer process. Huaxue Gongbao. 68, 8, p.3049-3055, DOI: 10.11949/j.issn.0438-1157.20161643.
Qing-tai Xiao, Jianxin Pan, Jian-xin Xu, Hua Wang and Zhi-han Lv (2017). Hypothesis-testing combined with image analysis to quantify evolution of bubble swarms in a direct-contact boiling heat transfer process. Applied Thermal Engineering. 113, 851-57, DOI: dx.doi.org/10.1016/j.applthermaleng.2016.11.004
Qiuji Li, Jianxin Pan and John Belcher (2016). Bayesian Inference for Joint Modelling of Longitudinal Continuous, Binary and Ordinal Events. Statistical Methods in Medical Research. 25, 6, 2521-2540, DOI: 10.1177/0962280214526199
Xiangzhao Cui, Chun Li, Jine Zhao, Li Zeng, Defei Zhang and Jianxin Pan (2016). Covariance Structure Regularization via Frobenius-Norm Discrepancy. Linear Algebra and Its Applications. 510, 124-145, DOI: 10.1016/j.laa.2016.08.013
Yu Fei, Yating Pan, Yin Chen and Jianxin Pan (2016). Modeling of covariance structures of random effects and random errors in linear mixed models. Communications in Statistics - Theory and Methods. 45, No. 9, 2748-2769, DOI: 10.1080/03610926.2015.1089290
Jianxin Xu, Qingtai Xiao, Yin Chen, Yu Fei, Jianxin Pan, Hua Wang (2016). A modified L2- star discrepancy method for measuring mixing uniformity in a direct contact heat exchanger. International Journal of Heat and Mass Transfer, 97: 70-76, DOI: 10.1016/j.ijheatmasstransfer.2016.01.064
Xiangzhao Cui, Chun Li, Jine Zhao, Li Zeng, Defei Zhang and Jianxin Pan (2016). Regularization for high-dimensional covariance matrix. Special Matrices. 4, 189-201, DOI: 10.1515/spma-2016-0018
Yu Fei, Qingtai Xiao, Jianxin Xu, Jianxin Pan, Shibo Wang, Hua Wang, Junwei Huang (2015). A novel approach for measuring bubbles uniformity and mixing efficiency in a direct contact heat exchanger. Energy. 93 (Part 2): 2313-2320, DOI: 10.1016/j.energy.2015.10.126
Yin Chen, Yu Fei and Jianxin Pan (2015) Statistical Inference in Generalized Linear Mixed Models by Joint Modelling Mean and Covariance of Non-Normal Random Effects. Open Journal of Statistics. 5: 568-584, DOI: 10.4236/ojs.2015.56059
Yin Chen, Yu Fei and Jianxin Pan (2015) Quasi-Monte Carlo Estimation in Generalized Linear Mixed Model with Correlated Random Effects. Open Access Library. 2: 1-16, DOI: 10.4236/oalib.1102002
Yin Chen, Yu Fei and Jianxin Pan (2015). The Effects of Covariance Structures on Modelling of Longitudinal Data. Open Access Library Journal, 2:e2086, DOI: 10.4236/oalib.1102086
Charalambous, C., Pan, J. and Tranmer, M. (2015). Variable selection in joint modelling of the mean and variance for hierarchical data. Statistical Modelling: An International Journal, 15(1): 24-50, DOI: 10.1177/1471082X13520424
Pan, J., Bao, Y., Dai, H. and Fang, H. (2014). Joint longitudinal and survival-cure models in tumour xenograft experiments. Statistics in Medicine, 33(18): 3229-3240, DOI: 10.1002/sim.6175
Charalambous, C., Pan, J. and Tranmer, M. (2014). Variable Selection in Joint Mean and Dis- persion Models via Double Penalized Likelihood. Sankhya B. 76(2): 276-304, DOI: 10.1007/s13571-014-0079-6
Pan, J., Fei Y., and Foster P. J. (2014). Case-deletion Diagnostics for Linear Mixed Models. Technometrics. 56(3): 269-281, DOI: 10.1080/00401706.2013.810173
Ha, I. D., Pan, J., Oh, S. and Lee, J. (2014). Variable Selection in General Frailty Models Using Penalized H-likelihood. Journal of Computational and Graphical Statistics. 23(4): 1044-1060, DOI: 10.1080/10618600.2013.842489
Pan, J. and Huang, C. (2014). Random effects selection in generalized linear mixed models via shrinkage penalty function. Statistics and Computing. 24(5): 725-738, DOI: 10.1007/s11222-013-9398-0
Lin, L. J., Higham, N. J. and Pan, J. (2014). Covariance structure regularization via entropy loss function. Computational Statistics and Data Analysis. 72, 315-327, DOI: 10.1016/j.csda.2013.10.004
Chen, Y., Tian, M., Yu, K. and Pan, J. (2014). Composite Hierachical Linear Quantile Regression. Acta Mathematicae Applicatae Sinica (English Series). 30, 49-64, DOI: 10.1007/s10255-014-0267-1
Li, D. and Pan, J. (2013). Empirical likelihood for generalized linear models with longitudinal data. Journal of Multivariate Analysis, 114, 63-73, DOI: 10.1016/j.jmva.2012.07.014
Lin, H. and Pan, J. (2013). Nonparametric estimation of mean and covariance structures for longitudinal data. The Canadian Journal of Statistics, 41, 557-574, DOI: 10.1002/cjs.11189
Dai, H., Pan, J. and Bao, Y. (2013). Modelling survival events with longitudinal data measured with error. Communications in Statistics: Theory and Methods, 42, 3819-3837, DOI: 10.1080/03610926.2011.624243.
Pan, J., Nummi, T. and Liu, K. (2013). Modeling of mean-covariance structures in generalized estimating equations with dropouts. Statistics and Its Interface, 6, No. 1, 19-26.
Nummi, T., Pan, J. and Mesue, N. (2013). Testing linearity in semiparametric regression models. Statistics and Its Interface, 6, No. 1, 3-8.
Mayukh Banerjee, Nilanjana Banerjee, Pritha Bhattacharjee, Debapriya Mondal, Paul R. Lythgoe, Mario Martez, Jianxin Pan, David A. Polya & Ashok K. Giri (2013). High Arsenic in Rice is Associated with Elevated Genotoxic Effects in Humans. Scientific Reports. 3, Article number: 2195, DOI: 10.1038/srep02195
C. Childs, K. Liu, A. Vail and J. Pan (2013). Time-dependent relationships between human brain and body temperature after severe traumatic brain injury. International Journal of Statistics in Medical Research, 2, 14-22, DOI: 10.6000/1929-6029.2013.02.01.02
M. J. Carr, J. Pan, R. McNamee and K. Cruickshank (2012). Long term treatment effects in the MRC elderly trial. Journal of Human Hypertension. 26(10): 617-618.
M. J. Carr, Y. Bao, J. Pan, K. Cruickshank, R. McNamee (2012). The predictive ability of blood pressure in elderly trial patients. Journal of Hypertension, 30, No.9, 1725-1733, DOI: 10.1097/HJH.0b013e3283568a73
T. Nummi, J. Pan, T. Siren and K. Liu (2011). Testing for Cubic Smoothing Splines under Dependent Data. Biometrics, 67, No. 3, 871-875, DOI: 10.1111/j.1541-0420.2010.01537.x
X. Huang, G. Li, R. Elashoff and J. Pan (2011). A general joint model for longitudinal mea- surements and competing risks survival data with heterogeneous random effects. Lifetime Data Analysis, 17, No. 1, 80-100, DOI: 10.1007/s10985-010-9169-6
R. Alhamzawi, K. Yu and J. Pan (2011). Prior elicitation in Bayesian quantile regression for longitudinal data. Journal of Biometrics & Biostatistics. 2, No. 1, 115-1-115-7, DOI: 10.4172/2155-6180.1000115
C. Childs, A. Ng, K. Liu and J. Pan (2011). Exploring the sources of missingness in brain tissue monitoring datasets: An observational cohort study. Brain Injury, 25, No. 12, 1163-1169, DOI: 10.3109/02699052.2011.607791
C. Leng, W. Zhang and J. Pan (2010). Semiparametric mean-covariance regression analysis for longitudinal data. Journal of the American Statistical Association, 105, No. 489, 181-193, DOI: 10.1198/jasa.2009.tm08485
Y. Fei, J. Pan and L. Wang (2009). Likelihood-based influence analysis in linear mixed models for longitudinal data. Journal of Systems Science and Mathematical Science, 29, No. 2, 271-279.
E. Al-Eid and J. Pan (2008). Randomized quasi-Monte Carlo estimation in generalized linear mixed models. Proceedings of Seminario Internacional Sobre Matemática Aplicadáy su Reper- cusión en las Sociedad Actual. Madrid, Spain, pp197-202.
C. Kou and J. Pan (2008). Variable selection in joint modelling of Mean and Covariance structures for longitudinal data. Proceedings of the 23rd International Workshop on Statistical Modelling. Utrecht, the Netherlands, pp309-314.
J. Pan and G. MacKenzie (2007). Modelling conditional covariance structures in linear mixed models. Statistical Modelling, 7, 49-71.
J. Pan and R. Thompson (2007). Quasi-Monte Carlo approximation for estimation in generalized linear mixed models. Computational Statistics and Data Analysis, 51, 5765-5775.
H. Ye and J. Pan (2006). Modelling covariance structures in generalized estimating equations for longitudinal data. Biometrika, 93, 927-941.
J. Pan and G. MacKenzie (2006). Regression models for covariance structures in longitudinal studies. Statistical Modelling, 6, 43-57.
J. Pan and D. von Rosen (2005). Modelling mean-covariance structures in the growth curve model. Contemporary Multivariate Analysis and Experimental Designs (Eds. J. Fan and G. Li), World Scientific, 141-157.
E. Al-Eid and J. Pan (2005). Parameter estimation in generalized linear mixed models using SNTO approximation. Proceeding of the 20th International Workshop on Statistical Modelling. Sydney, Australia, pp77-84
E. Tan and J. Pan (2005). Modelling the random effects covariance matrix for generalized linear mixed models using the GEM algorithm. Proceeding of the 55th Session of the International Statistical Institute (ISI), Sydney, Australia.
W. Syed, A. J. Pinkerton, L. Li, E. Al-Eid and J. Pan (2005). Statistical analysis of the effect of processing conditions on powder catchment efficiency in the Direct Laser Deposition (DLD) process. Virtual Modelling and Rapid Manufacturing - Advanced Research in Virtual and Rapid Prototyping (Eds. Paulo Jorge Bartolo, et al), pp361-367.
J. Pan (2004). Discordant outlier detection in the growth curve model with Rao’s simple covari- ance structure. Statistics & Probability Letters, 69, 135-142.
A. J. R. Cotter, L. Burt, C. G. M. Paxton, C. Fernandez, S. T. Buckland and J. Pan (2004). Are stock assessment methods too complicated Fish and Fisheries, 5, 235-254.
J. Pan and R. Thompson (2004). Quasi-Monte Carlo estimation in generalized linear mixed models. Proceedings of the 19th International Workshop on Statistical Modelling (Eds. Biggeri, A., Dreassi E., Lagazio C. Marchi M.), Florence, Italy. pp239-243.
H. Ye and J. Pan (2004). Modelling covariance structures in generalized estimating equations for longitudinal data. Proceedings of the 19th International Workshop on Statistical Modelling (Eds. Biggeri, A., Dreassi E., Lagazio C. Marchi M.), Florence, Italy. pp244-248.
J. Pan and G. MacKenzie (2004). Joint modelling of mean and covariance structures in longitu- dinal studies. Proceedings of the 2nd Workshop on Correlated Data Modelling: Common Ideas in Biometrices and Econometrics, Torino, Italy, pp16-19.
G. MacKenzie and J. Pan (2004). Optimal joint mean-covariance modelling. Proceedings of the 2nd Workshop on Correlated Data Modelling: Common Ideas in Biometrices and Econometrics, Torino, Italy, pp51-53.
J. Pan and G. MacKenzie (2003). Model selection for joint mean-covariance structures in longitudinal studies. Biometrika, 90, 239-244.
J. Pan and R. Thompson (2003). Gauss-Hermite quadrature approximation for estimation in generalised linear mixed models. Computational Statistics, 18, 57-78.
J. Pan and P. Bai (2003). Local influence analysis in the growth curve model with Rao’s simple covariance structure. Journal of Applied of Statistics, 30, 771-782.
J. Pan (2003). Discussion on IPM 20: Spatial and temporal modelling by residual maximum likelihood. Proceedings of the 54th Session of the International Statistical Institute (ISI), Berlin, Germany. Vol. LX, Book 3, pp26-27.
Y. Fei and J. Pan (2003). Influence assessments for longitudinal data in linear mixed mod- els. Proceedings of the 18th International Workshop on Statistical Modelling (Eds. Verbeke, G., Molenberghs, G., Aerts, A., and Fieuws, S.), Leuven, pp143-148.
G. Mackenzie and J. Pan (2003). Optimal model selection in a joint mean-covariance space. Proceedings of the 18th International Workshop on Statistical Modelling (Eds Verbeke, G., Molen- berghs, G., Aerts, A., and Fieuws, S.), Leuven, pp279-284.
J. Pan (2002). Influential observation identification in the growth curve model with Rao’s simple covariance structure. Communication in Statistics, (Theory and Methods), 31, 813-832.
J. Pan (2002). Modelling mean-covariance structures in the growth curve model. Proceedings of the 17th International Workshop on Statistical Modelling (Eds. M. Stasnopoulos and G. Touloumi), Chania, Greece, pp529-534.
J. Pan and G. MacKenzie (2001). Modelling conditional covariance structures in linear mixed models. Proceedings of the 16th International Workshop on Statistical Modelling (Eds. B. Klein and L. Korsholm), Odense, Denmark, pp491-494.
G. MacKenzie and J. Pan (2001). Modelling marginal covariance structures in linear mixed models. Proceedings of the 16th International Workshop on Statistical Modelling (Eds. B. Klein and L. Korsholm), Odense, Denmark, pp275-282.
J. Pan and W. K. Fung (2000). Bayesian influence assessment in the growth curve model with unstructured covariance. Annals of Institute of Statistical Mathematics, 52, 737-752.
J. Pan, W. K. Fung and K. T. Fang (2000). Multiple outlier detection in multivariate data using projection pursuit techniques. Journal of Statistical Planning and Inference. 83, 153-167.
J. Pan and R. Thompson (2000). Generalised linear mixed models: an improved estimating procedure. Proceedings in Computational Statistics (Eds. J.G. Bethlehem and P.G.M., van der Heijden), Physical-Verlag, pp373-378.
J. Pan, K. T. Fang and D. von Rosen (1999). Bayesian local influence in the growth curve model with unstructured covariance. Biometrical Journal, 41, 641-658.
K. T. Fang, W. C. Shiu and J. Pan (1999). Uniform Design Based on Latin Squares. Statistics Sinica, 9, 905-912.
J. Pan (1998). Discussion on the paper by James S. Hodges: Some algebra and geometry for hierarchical models applied to diagnostics. Journal of the Royal Statistical Society, Series B, 60, 532-533.
J. Pan, K. T. Fang and D. von Rosen (1998). On the posterior distribution of the covariance matrix of the growth curve model. Statistics & Probability Letters, 38, 33-39.
J. Pan and R. Thompson (1998). Quasi-Monte Carlo EM algorithm for estimation in gener- alised linear mixed models. Proceedings in Computational Statistics (Eds. Roger Payne and Peter Green), Physical-Verlag, 419-424.
J. Pan, K. T. Fang and D. von Rosen (1997). Local influence assessment in the growth curve model with unstructured covariance. Journal of Statistical Planning and Inference, 62, 263-278.
J. Pan, K. T. Fang and E. P. Liski (1996): Bayesian local influence in the growth curve model with Rao’s simple covariance structure. Journal of Multivariate Analysis, 58, 55-81
J. Pan and K. T. Fang (1996). Influential observations in the growth curve model with unstructured covariance matrix. Computational Statistics and Data Analysis, 22, 71-87.
P. Bai, J. Pan and X. Wang (1996). Bayesian influence analysis in a growth curve model (RSS). Chinese Journal of Applied Probability and Statistics, 12, 247-254.
J. Pan and K. T. Fang (1995). Multiple outlier detection in growth curve model with unstructured covariance matrix. Annals of Institute of Statistical Mathematics, 47, 137-153.
J. Pan and H. Xiong (1995). Outliers and influential observations in ridge mean-shift regression. Journal of Systems and Mathematical Sciences, 8, 12-26.
J. Pan, K. T. Fang and D. von Rosen (1995). Local influence for the growth curve model. Proceedings of the 50th Session of the International Statistical Institute (ISI). Beijing, China, 821-823.
J. Pan and X. Wang (1994). Unbiasedness of multivariate outlier test for elliptically contoured distribution. IMS Lecture Notes-Monograph Series: Multivariate Analysis and its Application (Eds. T. W. Anderson, K. T. Fang and I. Olkin), 24, 457-461.
X. Wang, M. Guo and J. Pan (1994). Admissibility of linear estimator of regression coefficients in growth curve model under matrix loss. Acta Mathematicae Applicatae Sinica, 10, 220-223.
J. Pan and K. T. Fang (1994). Multiple outlier detection in growth curve model with unstructured covariance. Proceedings of the Fifth Japan-China Symposium on Statistics, Tokoyo, Japan, 122-126.
X. Wang and J. Pan (1992). Optimal and robust detection of multivariate outliers for elliptically contoured distribution. Journal of Systems Science and Mathematical Sciences, 5, 146-163.
J. Pan (1991). Likelihood ratio criteria of parameters in growth curve model for multivariate elliptically contoured distribution. Chinese Journal of Applied Probability and Statistics, 7, 239-248.
W. Sun and J. Pan (1991). Principal Component Analysis of Geological Analogy. Proceedings of the Fourth China-Japan Symposium on Statistics, Kunming, China, 310-315.
J. Pan (1991). Outliers and influential observations in ridge mean shift regression. Proceedings of the Fourth China-Japan Symposium on Statistics, Kunming, China, 243-247.
X. Wang and J. Pan (1990). Robust detection and treatment of outliers in geological observations. Proceedings of International Workshop on Statistical Prediction of Mineral Resources, Wuhan, China, 43-49.
J. Pan (1989). Admissibility of linear estimator of regression coefficient in growth curve model. Acta Mathematicae Applicatae Sinica, 12, 456-465.
R. Stephens and J. Pan (1988). Identification of parameters of i.i.d. random variables via distribution of the k-th order statistic. Journal Yunnan University (Natural Science Edition), 10, 295-300.
J. Pan (1988). Generalised least square estimator of regression parameters and Gauss-Markov theorem in growth curve model. Journal of Mathematical Statistics and Applied Probability, 3, 169-185.
J. Pan (1987). Admissibility on linear estimator of regression coefficient in growth curve model. Proceedings of Sino-American Statistics Meeting, Beijing, China, 338-342.
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