史建清教授,南方科技大学统计与数据科学系教授。曾任英国纽卡斯尔大学(Newcastle University)统计学教授,英国国家艾伦图灵研究院图灵研究员。主要研究方向包括函数型数据分析,生物医学统计,缺失数据分析等。在国际学术刊物上发表高水平学术论文多篇,包括统计顶级期刊JRSSB,JASA,Biometrika和Biostatistics。曾任英国皇家统计协会《应用统计》副主编,英国纽卡斯尔大学云计算和大数据研究培训中心副主任。曾获邀任剑桥大学世界最顶级数学学院之一的牛顿学院访问研究员,获美国统计协会非参数统计分会年度最佳论文奖,2012年获英国 Wellcome trust Health Innovation Challenge Fund,共计210万英镑。2011年在著名统计学出版社Chapman & Hall 出版专著:Gaussian Process Regression Analysis for Functional Data。



1993-1996   博士(统计学),香港中文大学

1987-1989   硕士(统计学),东南大学

1980-1984   学士(计算数学),南京大学



2020.8-present  教授,南方科技大学统计与数据科学系

2018.10-2020.7  图灵研究员,英国国家艾伦图灵研究院

2015.7-2015.8  访问研究员,美国统计与应用数学科学研究所

2012.8-2020.7  教授,英国纽卡斯尔大学数学与统计系

2006.8-2012.8  高级讲师,英国纽卡斯尔大学数学与统计系

2008.2-2008.3  访问研究员,英国剑桥大学

2002-2006  讲师,英国纽卡斯尔大学数学与统计系

2000-2002  博士后,英国格拉斯哥大学

1998-2000  博士后,华威大学

1996-1998  助教,香港中文大学

1990-1993 and 1984-1987 助理讲师,东南大学



Fellow of the Royal Statistical Society, ICSA member



· Associate editor, Statistical and Probability letters (04/2016-)

· Associate editor, JRSSC (Applied Statistics, 01/2010-12/2013)

· Guest AE for JRSS discussion paper

· Mathematics prioritization panel member (2011,2012,2013)

· Member of APTS executive committee (10/2011-09/2012)

· Academic Editor. The Open Medical Informatics Journal.

· Academic Editor. British journal of Mathematics & Computer Sciences



□Handling missing data and time-varying confounding in causal inference for observational event history data. MRC council, PI (£270,510, Aug 2017 to Oct 2020).

□Limbs alive – monitoring of upper limb rehabilitation and recovery after stroke through gaming, Wellcome trust grant on HICF (Health Innovation Challenge Fund) with PI Prof. J A Eyre (total of 2.1 million pounds, Jan. 2012—Jan.2015).

-- One three-year full-time postdoctoral RA and one three-year half-time RA are based in the school of Maths & Stats and are under my supervision, working on statistical modelling, data analysis and validation.

-- I am the leader of Work Package 1: Data Analysis and Validation (one of four work packages).

□Predictive Dynamic Modelling for Next Generation Processing (PI, EPSRC Case Project, Jan. 2008-Jun. 2011, £60,864)

□Nonparametric Methods for Curve Fitting and Prediction with Large Data-set (PI, EPSRC, Jan. 2005-June 2007, £121,691, sole investigator)

□Predictive Dynamic Modelling for Next Generation Processing (PI, £83,500 from BP Oil international Ltd, Jan. 2008-Jun. 2011)

□Predictive Analysis (BP Postgraduate Project, Sept. 2007-Aug. 2009, £21,000, Principal Supervisor)

□Optimal Dynamic Control in Gaussian Process Regression Model (overseas collaborator), SG$ 50,420, PI: Dr. J. Li, NUS, Singapore



□`Gait Analytics’ has been successfully selected to demo at AI UK 2020 (leading by Prof. Paul Watson, Dr J. Q Shi is the leader of mathematical modelling).

□Guest Professor (from March 2013), Department of Mathematics, Southeast University (member of 985 group, a group of the top 39 Chinese universities).

□Serradilla, J. Shi, J. Q., Cheng, Y., Morgan, G., Lambden, C. and Eyre, J. A. (2014). The best paper winner in the IEEE 3rd International Conference on Serious Games and Applications for Health, held on Rio de Janeiro, Brazil, May 14-16, 2014 ).

□Choi, Shi and Wang (2011) won 2011 best paper award by the Journal of Nonparametric Statistics (JNSP) and American Statistical Association on Nonparametric Statistics. The most read article of JNSP (465 views since the website was launch in June 2011, updated 04/11/2013)

□ESRC-funded Project R000237498: Methodology for Meta-analysis and the What Works Debate is graded as Outstanding: ‘High quality research making an important contribution to the development of the subject’. (J.B. Copas and J.Q. Shi, 2000).

□Award of Advanced Science and Technology, Chinese National Education Committee, 1996. Topic: Nonlinear Statistical Models and Nonlinear Diagnostics Methods (B.C. Wei, J.Q. Shi, G.B. Lu, F.H. Wan and Y.Q. Hu).

Ph.D. Graduate of the year, The Chinese University of Hong Kong, 1996.

□Kam Ngan Stock Exchange Scholarship 1994/95. The Chinese University of Hong Kong.



o Invited speaker in Internal Statistical Conference in memory of Prof. SY Lee (18-19, Dec, Hong Kong, China)

o Invited talk: 2019 IMS China Meeting - Institute of Mathematical Statistics (6-10, July 2019, Dalian, China)

o Invited talk: ICSA China Conference (July 1-4, 2019, Tianjin, China)

o Invited participant of MATRIX programme: functional data analysis and beyond. 2- 14, Dec, 2018

o CMStatistics, 16-18 Dec 2017, London. Invited talk and organizer of one invited session.

o July, ISI 2017. Invited talk, Morocco, Marrakech.

o June 2016. invited talk, Symposium on Frontiers of Statistics and Data Sciences, Hong Kong.

o March 2016. Invited short course on Functional Data Analysis, National School of Statistics and Information Analysis (ENSAI), Rennes, France

o Jan. 2016. 2nd UCL Workshop on the Theory of Big Data, invited talk

o Dec. 2015 CFE-CMStatistics 2015. Member of scientific program committee. Organizer and speaker of invited talk sessions.

o Invited participant and speaker. BIRS Workshop. Frontiers in Functional Data Analysis, Canada, 2014 (local expense including accommodation is supported by the organizer).

o ERCIM 2014. Invited talk in Bayesian semiparametric inference session: Nonlinear mixed-effects GP functional regression models with applications to motion data.

o John Nelder Memorial Session: Functional data analysis for high dimensional and complex data. Joint meeting of the IASC Satellite conference and the 8th conference of the Asian Regional Section of the IASC. August 2013, Seoul, South Korea.

o ASC Satellite Conference SRC SYMPOSIUM. Invited talk (2 hours): Gaussian process regression analysis for functional data, August 2103, Seoul, South Korea.

o ICSA 2013 Applied Statistics Symposium / ISBS International Symposium on Biopharmaceutical Statistics Joint Meeting, invited talk: Bayesian GP regression analysis for large functional data. June 2013, Bethesda, Maryland, USA.

o International Workshop on the Perspectives on High-dimensional Data Analysis III, invited talk: Nonlinear Curve Fitting and Clustering Using GPR Models May 2013,

o Vancouver. Functional regression and classification (3 talks). April 2013, Shandong University. China.

o Gaussian process regression analysis for functional data and applications. April 2013, Fudan University, China.

o Model misspecification and bias analysis (4 talks). March 2013, Southeast University, China.

o 12/09/2012, invited talk given in the workshop of “High dimensional and dependent functional data”, 10-12 September 2012, Bristol, UK (all expenses are paid by the organizer).

o 28/08/2012, Invited talk given in the session “Novel Mixture Modelling and Likelihood methods in Modern Biomedical Applications”, IBC, Kobe, 26-30/08/2012.

o 16/08/2012, invited talk: “Nonlinear Curve Fitting and Clustering Using GPR Models”, Korea university.

o 09/04/2012, Open lecture for undergraduate students: “Statistics and Applications”, Nanjing University of Information Science and Technology.

o 26/03--09/04/2012, invited 3 series talks: “Missing Data, Model Misspecification and Sensitivity Analysis”, Nanjing University of Information Science and Technology .

o 27/03—08/04/2012, invited 4 series talks “Nonlinear Functional Data Analysis”, Southeast University.

· I have been awarded Turing fellow, Alan Turing Institute (from 10/18)

· Co-Chair of the local organizing committee, 2012 Pre-Olympic Congress, July 24–25, 2012 at ACC Liverpool, UK, including Sports Science and Computer Science in Sport (IACSS2012), 5th International Conference on Information and Computing Science (ICIC2012), and 5th International Conference on Modelling and Simulation (ICMS2012).

· Won the best paper award in the IEEE 3rd International Conference on Serious Games and Applications for Health in 2014 and the best paper award by the Journal of Nonparametric Statistics and American Statistical Association on Nonparametric Statistics in 2011.

· I am invited to participate the programme on Challenges in Computational Neuroscience at the Statistical and Applied Mathematical Sciences Institute in 2015, working on the programme with financial support. I am invited to participate at the programme of Statistical Theory and Methods for Complex, High-Dimensional Data and worked in Isaac Newton Institute of Cambridge University as a visiting research fellow for two months (with financial support), Feb. – March 2008.

· BP Oil international Ltd paid £62,500 to Newcastle University and the investigators for buying intellectual property on applying statistical methods to process monitoring and malfunction detection. I am the PI of the research projects. 2011.

· Some research papers have high citation rate (Google Scholar: 1964 citations with h- index of 24 and i10-index of 28, and 996 citations with h-index of 16 and i10-index of 24 from 2014, updated on 23/12/2019).

· Developed CRAN R-package GPFDA: Gaussian Process in Functional Data Analysis (2015) and R-package fLARS (2016)



The top journals in statistics include the Journal of Royal Statistical Society Series B, Journal of American Statistical Association, Biometrika, Biometrics and Biostatistics. Leading statistical journals include, Statistics and Computing, Statistica Sinica, Psychmrtrika, Statistics in Medicine and others. In application areas, the leading journals are British Medical Journal, Chemometrics and Intelligent Laboratory Systems, IEEE Transactions on Neural Systems & Rehabilitation Engineering and others.


1.      Shi, J. Q. and Choi. T. (2011). Gaussian Process Regression Analysis for Functional Data. Chapman & Hall, CRC.

2.      Wei, B.C., Lu.,G.P. and Shi, J.Q. (1991). Introduction of Statistical Diagnostics.

Southeast University Press, Nanjing. (in Chinese)


1.      Cheng, Y. and Shi, J. Q. (2016). R-package: fLARS: functional variable selection using fLARS.

2.      Shi, J. Q. and Cheng, Y. (2015). R-package GPFDA: Gaussian Process in Functional Data Analysis


· Konzen, E., Shi, J. Q. and Wang, Z. (2019). Modelling Function-valued Processes with Nonseparable Covariance Structure. arXiv:1903.09981.

·  Wang, Z., Noh, M., Lee, Y. and Shi, J. Q. (2019). A robust t-process estimation approach for functional regression            model                  with               batch          data. arXiv:1707.02014

· Wang, Z, Li, K. and Shi, J. Q. (2019). A robust estimation for the extended t-process regression models.

· Yang, X, Shi, J. Q. and Fu, B. (2019) Identification of Causal Effects and Sensitivity Analysis with Confounders Missing Not at Random

· Zeng, P., Tang, L., Shi, J. Q. and Kim, W-S (2019) Joint Curve Registration and Classification with Two-level Functional Models

· Tang, L., Halloran, S., Shi, J. Q., Guan, Y., Cao, C. and Eyre, J. (2019). Evaluating upper limb function after stroke using the free-living accelerometer data

· Yin, P., Zhu, R. and Shi, J. Q. (2019) Interpretable drivers of sensitivity analysis for non- ignorable missing covariate in linear regression models _


· Shi, J and Konzeri, E (2018) discussion on ‘The statistical analysis of acoustic phonetic data: exploring differences between spoken Romance languages’ by D. Pigoli, P. Z. Hadjipantelis, J. S. Coleman and J. A. D. Aston, Appl. Statist. 67.


  1. Tang, L., Halloran, S., Shi, J. Q., Guan, Y., Cao, C. and Eyre, J. (2020). Evaluating upper limb function after stroke using the free-living accelerometer data. Statistical Methods in Medical Research (accepted.)
  2. Cheng, Y., Shi, J. Q. and Eyre, J. (2020) Nonlinear Mixed-effects Scalar-on-function Models and Variable Selection for Kinematic Upper Limb Movement Data. arXiv:1605.06779 Statistics and Computing, 30, 129-140. Available on line now. https://link.springer.com/article/10.1007%2Fs11222-019-09871-3
  3. Wang, Z., Ding, H, Chen, Z and Shi, J. Q. (2020). Nonparametric Random Effect Functional Regression Model with Gaussian process priors. Statistica Sinica.  Available on line now: http://www3.stat.sinica.edu.tw/ss_newpaper/SS-2018-0296_na.pdf
  4. Sofro, A., Shi, J. Q. and Cao, C. (2020) Regression Analysis for Multivariate Process Data of Counts using Convolved Gaussian Processes. Journal of Statistical Planning and Inference. 206 , 57-74 (online version is available now).
  5. Chunzheng Cao, Ziyue Wang, Jian Qing SHI and Yunjie Chen (2020) Robust Task Learning Based on Nonlinear Regression with Mixtures of Student-t Distributions. IEEE Access.
  6. Coates, L, Shi, J. Q., Rochester, L, Del Din, S. and Pantall, A. (2020). Entropy of Real-World Gait in Parkinson’s Disease Determined from Wearable Sensors as a Digital Marker of Altered Ambulatory Behavior. Sensors, 20(9). 2631; https://doi.org/10.3390/s20092631.
  7. Zeng, P., Shi, J. Q. and Kim, W-S (2019) Simultaneous registration and clustering for multidimensional functional data. of Computational and Graphical Statistics. 28, 943-953. arXiv:1711.04761. Available on line now https://www.tandfonline.com/doi/full/10.1080/10618600.2019.1607744.
  8. Yin, P. and Shi, J. Q. (2019) Simulation-based Sensitivity Analysis for Non-ignorable Missing Data.Statistical Methods in Medical Research, 28(1) 289–308. http://journals.sagepub.com/doi/pdf/10.1177/0962280217722382. arXiv:1501.05788.
  9. Xu, P, Lee, Y., Shi, J.Q. and Eyre, J. (2019) Automatic Detection of Significant Areas for Functional Data with Directional Error Control. arXiv: 08164. Statistics in medicine, 38, 376-397. https://doi.org/10.1002/sim.7968. Available online now. https://onlinelibrary.wiley.com/doi/full/10.1002/sim.7968
  10. Rehman, R. Z. U., Del Din, S., Guan, Y., Yarnall, A. J., Shi, J. Q. and Rochester, L. (2019). Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson’s Disease: A Comprehensive Machine Learning Approach. Scientific Reports. https://www.nature.com/articles/s41598-019-53656-7
  11. Rehman, R. Z. U., Del Din, S., Shi, J. Q., Galna, B., Lord, S., Yarnall, A. J., Guan, Y. and Rochester, L. (2019). Comparison of Walking Protocols and Gait Assessment Systems for Machine Learning-Based Classification of Parkinson’s Disease. Sensors. https://www.mdpi.com/1424-8220/19/24/5363
  12. Rehman, R. Z. U., Buckley, B., Mico-Amigo, M. E., Kirk, C., Dunne-Willows, M., Mazza, C., Shi, J. Q., Alcock, L., Rochester, L. and Del Din, S. (2019).  Accelerometry-based digital gait characteristics for classification of Parkinson’s disease: what counts? IEEE Open Journal of Engineering in Medicine and Biology (accepted).
  13. Zhanfeng Wang, Kai Lia, Jian Qing Shi (2019). A robust estimation for the extended t-process regression model. Statistics and Probability Letters. Available on line now: https://www.sciencedirect.com/science/article/pii/S016771521930272X?dgcid=coauthor.
  14. Halloran, S., Tang, L., Guan, Y., Shi, J. Q., and Eyre, J. (2019). Remote monitoring of stroke patients' rehabilitation using wearable accelerometers. In Proceedings of the 2019 ACM International Symposium on Wearable Computers. ACM.
  15. Shi, J. Q. (2018). How Do Statisticians Analyse Big Data – Our story. Statistics and Probability Letters. 136,130-133.  https://doi.org/10.1016/j.spl.2018.02.043.
  16. Cao, C., Chen, M., Wang, Y and Shi, J. Q. (2018) Heteroscedastic replicated measurement error models under asymmetric heavy-tailed distributions. Computational Statistics, 33, 319-338.
  17. Cao, C., Wang, Y., Shi, J. Q. and Lin, J. (2018) Measurement Error Models for Replicated Data Under Asymmetric Heavy-Tailed Distributions. Computational Economics,  33, 319-338.https://doi.org/10.1007/s10614-017-9702-8.
  18. Cao, C., Shi, J. Q. and Lee, Y. (2018). Robust functional regression model for population-average and subject-specific inferences. Statistical Methods in Medical Research, 27, 3236-3254. http://journals.sagepub.com/doi/10.1177/0962280217695346.
  19. Kim, W-S, Zeng, P., Shi, J. Q., Lee, Y. and Paik, N-J. (2017)Automatic tracking of hyoid bone motion from videofluoroscopic swallowing study with automatic smoothing and segmentation. PLOS ONE, https://doi.org/10.1371/journal.pone.0188684.
  20. Wang, Z., Shi, J. Q. and Lee, Y. (2017) Extended T-process Regression Models. Journal of Statistical Planning and Inference, 189,38-60. arXiv:1705.05125.
  21. Cao, C., Lin, J, Shi, J. Q., Wang, W. and Zhang, X. (2015) Multivariate measurement error models for replicated data under heavy-tailed distributions Journal of Chemometrics, 29, 457-466.
  22. Lu, H., Yin, P., Yue, R. X. and Shi, J. Q. (2015) Robust confidence intervals for trend estimation in meta-analysis with publication bias. Journal of Applied Statistics, 42, 2715-2733.
  23. Wang, B. and Shi, J. Q. (2014). Generalized Gaussian Process Regression Model for non-Gaussian Functional Data. Journal of American Statistical Association, 109, 1123-1133.
  24. Serradilla, J. Shi, J. Q., Cheng, Y., Morgan, G., Lambden, C. and Eyre, J. A. (2014). Automatic Assessment of Upper Limb Function During Play of the Action Video Game, Circus Challenge: Validity and Sensitivity to Change. SEGAH 2014. (The best paper winner in the IEEE 3rd International Conference on Serious Games and Applications for Health, held on Rio de Janeiro, Brazil, May 14-16, 2014 ).
  25. Scott M, Blewitt W, Ushaw G, Shi JQ, Morgan G, Eyre J. Automating assessment in video game teletherapy: Data cutting.In: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE). 2014, Orlando, Florida: IEEE. 9-16.
  26. Cao, C. Z., Lin, J. G. and Shi, J. Q. (2014). Diagnostics on nonlinear model with scale mixtures of skew-normal and first-order autoregressive errors. Statistics. ,48, 1033-1047.
  27. Shi, J.Q, Cheng, Y., Serradilla, J., Morgan, G., Lambden, C., Ford, G.A., Price, C., Rodgers, H, Cassidy, T., Rochester, L. and Eyre, J.A. (2013). Evaluating Functional Ability of Upper Limbs after Stroke Using Video Game Data. Imamura et al. (Eds.): BHI 2013, LNAI 8211, pp. 181–192. Springer.
  28. Lin, N. X., Shi, J. Q. and Henderson, R. (2012). Doubly mis-specified models. Biometrika. 99, 285-298.
  29. Shi, J. Q., Wang, B., Will, E. J. and West. R. M. (2012). Mixed-effects GPFR models with application to dose-response curve prediction. Statistics in Medicine. 31, 3165-77.
  30. Yi, G., Shi, J. Q. and Choi, T. (2011) Penalized Gaussian Process Regression and Classification for High-Dimensional Nonlinear Data. 67, 1285-1294.
  31. Choi, T., Shi, J. Q. and Wang, B. (2011). Bayesian single-index model using a Gaussian process prior. of Nonparametric Statistics. 23, 21-36. (The winning paper for the JNPS and American Statistical Association Nonparametric Section 2011 Best Paper Award)
  32. Serradilla, J., Shi, J.Q. and Morris, J. (2011). Fault detection based on Gaussian process latent variable models. Chemometrics and Intelligent Laboratory Systems. 109, 9-21.
  33. Chootrakool, H., Shi, J. Q. and Yue, R. (2011). Meta-analysis and sensitivity analysis for multi-arm trials with selection bias. Statistics in Medicine. 30, 1183-1198.
  34. Lee, W., Shi, J. Q. and Lee, Y. (2010). Approximate Conditional Inference in Mixed Effects Models with Binary Data. Computational Statistics and Data Analysis. 54, 173-184.
  35. Shi, J. Q. and Tang, M. L. (2009). Trend estimation (2nd edition). Encyclopaedia of Biopharmaceutical Statistics, 1:1, 1 – 7 URL: http://dx.doi.org/10.1081/E-EBS-120041929 . (Invited contribution for an entry to the Encyclopedia of Biopharmaceutical Statistics, peer-reviewed)
  36. Chootrakool, H. and Shi, J. Q. (2008). Meta-Analysis of multi-arm trials using Empirical Logistic Transform. The Open Medical Informatics Journal, 2. 105-109.
  37. Shi, J.Q. and Wang, B. (2008) Curve Prediction and Clustering with Mixtures of Gaussian Process Functional Regression Models. Statistics and Computing, 18, 267-283.
  38. Shi, J.Q., Wang, B., Murray-Smith, R. and Titterington, D.M. (2007). Gaussian process functional regression modeling for batch data. Biometrics, 63, 714-723.
  39. Shi, J. Q. (2007). Meta-analysis and latent variable models for binary data. Handbook of Computing and Statistics with Applications: Latent Variable and Related Models, 1. Lee, S.Y., Kontoghiorghes, E.J., Eds.; Elsevier,2007, 261-278 .
  40. Shi, J.Q., Murray-Smith, R. and Titterington, D.M. (2005). Hierarchical Gaussian process mixtures for regression. Statistics and Computing 15, 31-41.
  41. Kamnik, R., Shi, J.Q., Murray-Smith, R. and Bajd, T. (2005). Nonlinear modelling of FES-supported standing up in paraplegia for selection of feedback sensors. IEEE Transactions on Neural Systems & Rehabilitation Engineering, 13, 40-52.
  42. Shi, J. Q., Murray-Smith, R., Titterington, D.M. and Pearlmutter, B.A. (2005). Learning with large data-sets using a filting approach. Switching and Learning in Feedback Systems. Eds R. Murray-Smith and R. Shorten. Springer-verlag. 128-139.
  43. Shi, J.Q. and Copas, J.B. (2004). Meta-analysis for trend estimation. Statistics in Medicine. 23, 3-19.
  44. Shi, J.Q., Murray-Smith, R. and Titterington, D.M. (2003). Bayesian regression and classification using mixtures of multiple Gaussian processes. International Journal of Adaptive Signal Processing. 17, 149-161.
  45. Shi, J.Q. and Copas, J.B. (2002). Meta-analysis for 22 Tables using an average Markov chain Monte Carlo EM algorithm. R. Statist. Soc. B. 64, 221-236.
  46. Shi, J.Q., Lee, S.Y. and Wei, B.C. (2002). On confidence regions of structural equation Models. In Structural Equation and Latent Variable Models. Eds. G. A. Marcoulides and I. Moustaki. Lawrence Erlbaum Associatites, INC. 135-152.
  47. Lee, S.Y. and Shi, J.Q. (2001). Maximum likelihood estimation of two-level latent variable models with mixed continuous and polytomous data. Biometrics, 57, 787-794.
  48. Copas, J.B. and Shi, J.Q. (2001). A Sensitivity analysis for publication bias in systematic reviews. Statistical Methods in Medical Research. 10, 251-265.
  49. Shi, J.Q. and Lee, S.Y. (2000). Latent Variable models with mixed continuous and polytomous data. R. Statist. Soc. B, 62, 77-87.
  50. Copas, J.B. and Shi, J.Q. (2000). Meta-analysis, funnel plots and sensitivity analysis. Biostatistics, 1, 247-262.
  51. Copas, J.B. and Shi, J.Q. (2000). The epidemiological evidence on lung cancer and passive smoking: a reanalysis. British Medical Journal, 320, 417-418.
  52. Lee, S.Y. and Shi, J.Q. (2000). Joint Bayesian analysis of factor score and structural parameters in the factor analysis models. Inst. Statist. Math, 52, 722-736.
  53. Lee, S.Y. and Shi, J.Q. (2000). Bayesian analysis of structural equation model with fixed covariates. Structural Equation Modelling, 7, 411-430.
  54. Shi, J.Q. and Wan, F.H. (1999). Diagnostics for empirical Bayes models. Systems Science and mathematical Sciences, 12, No.2, 104-114.
  55. Lee, S.Y. and Shi, J.Q. (1998). Analysis of covariance structure with independent and non-identically distributed observations. Statistica Sinica, 8, 543-557.
  56. Shi, J.Q. and Lee, S.Y. (1998). Bayesian sampling-based approaches for factor analysis model with continuous and polytomous data J. of Math. & Stat. Psychology, 51, 233- 252.
  57. Wei, B.C., Shi, J.Q., Fung, W.K. and Hu, Y.Q. (1998). Testing for varying dispersion in exponential family nonlinear models. Inst. Statist. Math. 50, No.2, 277-294.
  58. Shi, J.Q. and Lee, S.Y. (1997). A Bayesian estimation of factor score in confirmatory factor model with polytomous, censored or truncated data. Psychometrika, 62, No.1, 29-50.
  59. Shi, J.Q. and Lee, S.Y. (1997). Estimation of factor scores with polytomous data by EM algorithm. J. of Math. \& Stat. Psychology, 50, 215-226.
  60. Shi, J.Q. and Wei, B.C. (1995). Bayesian local influence. Mathematica Applicata, 8, No.2, 237-245.
  61. Shi, J.Q. and Wei, B.C. (1995). Diagnostics for nonlinear regression model with transformation or weighting. Systems Science and Mathematical Sciences, 8, No.3, 240-248.
  62. Shi, J.Q. (1995). Assessing local prior influence in Bayesian analysis. Applied Mathematics - A journal of Chinese University, 10B, 123-132.
  63. Wei, B.C. and Shi, J.Q. (1994). On statistical models for regression diagnostics. Inst. Statist. Math., 46, No. 2, 267-278.
  64. Wei, B.C. and Shi, J.Q. (1994). Local influential analysis for transformation model. Acta Mathematicae Applicatae Sinica, 17, No.1, 132-143. (in Chinese).
  65. Shi, J.Q. (1993). Regression transformation diagnostics in transform-both-sides model. Statistics \& Probability Letters, 16, 411-20.
  66. Hu, H.M. and Shi, J.Q. (1993). Estimation of matrix elliptical contoured distributions with censored data. J. of Southeast University, 23, No.3, 118-22. (in Chinese).
  67. Shi, J.Q. (1993). Bayesian inference for change points in mathematical models of social economy. Information & Control, 22, 165-9. (in Chinese).
  68. Shi, J.Q. (1993). Influential analysis in accelerate life model with censored data. & Elec. Tech. Water Conservancy & Elec. Pow.}, V7, No.1, 53-58. (in Chinese).
  69. Shi, J.Q. (1992). Diagnostics and local influence for the proportional hazards model with censored data. of Biomathematics, Vol.7, No.3, 81-91. (in Chinese).
  70. Shi, J.Q. and Wei, B.C. (1992). Convergence of iteration methods of maximum likelihood estimator and its applications. of Southeast University (English Edition), V.8, No.2, 85-93; (Chinese Edition), V.22, No.3, 81-88.
  71. Shi, J.Q. and Zhao, Z.F. (1992). A predictive view of inference about the structural change in mathematical models of social economy. Information & Control, 21, 271-7. (in Chinese).
  72. Shi, J.Q. and Yao, Q.W. (1989). A Bayesian Inference for a change point in a multivariate linear model. Mathematical Statistics and Applied Probability, 4, 297-307. (in Chinese).