Dr. Jian Qing SHI, the Professor of Department of Statistics and Data Science, Southern University of Science and Technology. He used to be Reader in Statistics of Newcastle University and Assistant Director of Cloud Computing for Big Data CDT. He is Turing Fellow of Alan Turing Institute in UK. His research interests include Regression analysis, Canonical correlation analysis, Machine learning. He has published many papers in the top journals in statistics include the Journal of Royal Statistical Society Series B, Journal of American Statistical Association, Biometrika, Biometrics and Biostatistics. In 2012, Dr. SHI received Health Innovation Challenge Fund with PI Prof. J A Eyre (total of 2.1 million pounds, Jan. 2012—Jan.2015). In 2011, he published a book Gaussian Process Regression Analysis for Functional Data with Choi. T. in Chapman & Hall, CRC.
1. 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.
2. 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 AssessmentSystems for Machine Learning-Based Classification of Parkinson’s Disease. Sensors. https://www.mdpi.com/1424-8220/19/24/5363.
3. 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).
4. Sofro, A., Shi, J. Q. and Cao, C. (2019) Regression Analysis for Multivariate Process Data of Counts using Convolved Gaussian Processes. Journal of Statistical Planning and Inference (in press) arXiv: 1710.01523.
5. 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.
6. Wang, Z., Ding, H, Chen, Z and Shi, J. Q. (2019). 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
7. Yin, P. and Shi, J. Q. (2019) Simulation-based Sensitivity Analysis for Non-ignorable Missing Data. Statistical Methods in Medical Research, Vol. 28(1) 289–308. http://journals.sagepub.com/doi/pdf/10.1177/0962280217722382. arXiv:1501.05788.
8. Cheng, Y., Shi, J. Q. and Eyre, J. (2019) Nonlinear Mixed-effects Scalar-on-function Models and Variable Selection for Kinematic Upper Limb Movement Data. arXiv:1605.06779 Statistics and Computing, Available on line now. https://link.springer.com/article/10.1007%2Fs11222-019-09871-3
9. Zeng, P., Shi, J. Q. and Kim, W-S (2019) Simultaneous registration and clustering for multidimensional functional data. arXiv:1711.04761. J. of Computational and Graphical Statistics. Available on line now https://www.tandfonline.com/doi/full/10.1080/10618600.2019.1607744.
10. Xu, P, Lee, Y., Shi, J.Q. and Eyre, J. (2019) Automatic Detection of Significant Areas for Functional Data with Directional Error Control. arXiv: 1504.08164. Statistics in medicine, https://doi.org/10.1002/sim.7968. Available online now. https://onlinelibrary.wiley.com/doi/full/10.1002/sim.7968
11. 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.
12. 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.
13. 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.
14. 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.
15. 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.
16. 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.
17. Cao, C., Shi, J. Q. and Lee, Y. (2017). Robust functional regression model for population- average and subject-specific inferences. Statistical Methods in Medical Research, http://journals.sagepub.com/doi/10.1177/0962280217695346.
18. 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.
19. 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.
20. 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.
21. 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 ).
22. 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.
23. 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.
24. 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. K. Imamura et al. (Eds.): BHI 2013, LNAI 8211, pp. 181–192. Springer.
25. Lin, N. X., Shi, J. Q. and Henderson, R. (2012). Doubly mis-specified models. Biometrika. 99, 285-298.
26. 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.
27. Yi, G., Shi, J. Q. and Choi, T. (2011) Penalized Gaussian Process Regression and Classification for High-Dimensional Nonlinear Data. Biometrics. 67, 1285-1294.
28. Choi, T., Shi, J. Q. and Wang, B. (2011). Bayesian single-index model using a Gaussian process prior. J. of Nonparametric Statistics. 23, 21-36. (The winning paper for the JNPS and American Statistical Association Nonparametric Section 2011 Best Paper Award)
29. 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.
30. 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.
31. 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.
32. 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)
33. 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.
34. 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.
35. 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.
36. 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, Vol. 1. Lee, S.Y., Kontoghiorghes, E.J., Eds.; Elsevier,2007, 261-278 .
37. Shi, J.Q., Murray-Smith, R. and Titterington, D.M. (2005). Hierarchical Gaussian process mixtures for regression. Statistics and Computing 15, 31-41.
38. 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.
39. 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.
40. Shi, J.Q. and Copas, J.B. (2004). Meta-analysis for trend estimation. Statistics in Medicine. 23, 3-19.
41. 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.
42. Shi, J.Q. and Copas, J.B. (2002). Meta-analysis for 22 Tables using an average Markov chain Monte Carlo EM algorithm. J. R. Statist. Soc. B. 64, 221-236.
43. 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.
44. 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.
45. 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.
46. Shi, J.Q. and Lee, S.Y. (2000). Latent Variable models with mixed continuous and polytomous data. J. R. Statist. Soc. B, 62, 77-87.
47. Copas, J.B. and Shi, J.Q. (2000). Meta-analysis, funnel plots and sensitivity analysis.Biostatistics, 1, 247-262.
48. 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.
49. Lee, S.Y. and Shi, J.Q. (2000). Joint Bayesian analysis of factor score and structural parameters in the factor analysis models. Ann. Inst. Statist. Math, 52, 722-736.
50. Lee, S.Y. and Shi, J.Q. (2000). Bayesian analysis of structural equation model with fixed covariates. Structural Equation Modelling, 7, 411-430.
51. Shi, J.Q. and Wan, F.H. (1999). Diagnostics for empirical Bayes models. Systems Science and mathematical Sciences, 12, No.2, 104-114.
52. Lee, S.Y. and Shi, J.Q. (1998). Analysis of covariance structure with independent and non-identically distributed observations. Statistica Sinica, 8, 543-557.
53. Shi, J.Q. and Lee, S.Y. (1998). Bayesian sampling-based approaches for factor analysis model with continuous and polytomous data Brit. J. of Math. & Stat. Psychology, 51, 233- 252.
54. Wei, B.C., Shi, J.Q., Fung, W.K. and Hu, Y.Q. (1998). Testing for varying dispersion in exponential family nonlinear models. Ann. Inst. Statist. Math. 50, No.2, 277-294.
55. 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.
56. Shi, J.Q. and Lee, S.Y. (1997). Estimation of factor scores with polytomous data by EM algorithm. Brit. J. of Math. & Stat. Psychology, 50, 215-226.
57. Shi, J.Q. and Wei, B.C. (1995). Bayesian local influence. Mathematica Applicata, 8, No.2, 237-245.
58. 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.
59. Shi, J.Q. (1995). Assessing local prior influence in Bayesian analysis. Applied Mathematics – A journal of Chinese University, 10B, 123-132.
60. Wei, B.C. and Shi, J.Q. (1994). On statistical models for regression diagnostics. Ann. Inst. Statist. Math., 46, No. 2, 267-278.
61. 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).
62. Shi, J.Q. (1993). Regression transformation diagnostics in transform-both-sides model.Statistics & Probability Letters, 16, 411-20.
63. Hu, H.M. and Shi, J.Q. (1993). Estimation of matrix elliptical contoured distributions with censored data. J. of Southeast University, V.23, No.3, 118-22. (in Chinese).
64. Shi, J.Q. (1993). Bayesian inference for change points in mathematical models of social economy. Information & Control, 22, 165-9. (in Chinese).
65. Shi, J.Q. (1993). Influential analysis in accelerate life model with censored data. Mach. & Elec. Tech. Water Conservancy & Elec. Pow.}, V7, No.1, 53-58. (in Chinese).
66. Shi, J.Q. (1992). Diagnostics and local influence for the proportional hazards model with censored data. J. of Biomathematics, Vol.7, No.3, 81-91. (in Chinese).
67. Shi, J.Q. and Wei, B.C. (1992). Convergence of iteration methods of maximum likelihood estimator and its applications. J. of Southeast University (English Edition), V.8, No.2, 85-93; (Chinese Edition), V.22, No.3, 81-88.
68. 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).
69. 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).