Book

Gaussian Process Regression Analysis for Functional Data Jian Qing Shi and Taeryon Choi Chapman & Hall/CRC Press (also available at Amazon). (Matlab codes are available for some examples.) R package GPFDA is available now (Download Manual here)

• Shi, J. Q. and Choi, T. (2011). Gaussian Process Regression Analysis for Functional Data Chapman & Hall/CRC Press (also available at Amazon). [Matlab codes for some examples are available].
• Wei, B.C., Lu, G.P and Shi, J.Q. (1991). Introduction of Statistical Diagnostics. Southeast University Press, Nanjing (in Chinese).

Papers

Selected Manuscripts

• Evandro Konzen, Jian Qing Shi and Zhanfeng Wang (2019). Modelling Function-valued Processes with Nonseparable Covariance Structure. arXiv:1903.09981
• 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.

Discussions

• Shi, J. Q. and Halloran, S. (2019) Comments on: `Data science, big data and statistics' by P. Galeano and D. Pena, Test online version of the paper and online version of the discussion.
• 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.

Selected published papers (peer-reviewed)

1. 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).
2. 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. Statistics and Computing. online version is available now. arXiv:1605.06779
3. Zeng, P., Shi, J. Q. and Kim, W-S (2019) Simultaneous registration and clustering for multidimensional functional data. J. of Computational and Graphical Statistics. 28 , 943-953, online version is available now. arXiv:1711.04761
4. Yin, P. and Shi, J. Q. (2019) Simulation-based Sensitivity Analysis for Non-ignorable Missing Data. Statistical Methods in Medical Research, 28 , 289-308. (online version is available now). arXiv:1501.05788
5. Xu, P, Lee, Y., Shi, J. Q. and Eyre, J. (2019) Automatic Detection of Significant Areas for Functional Data with Directional Error Control. Statistics in Medicine, (online version is available now) arXiv:1504.08164
6. Wang, Z., Ding, H, Chen, Z and Shi, J. Q. (2019). Nonparametric Random Effect Functional Regression Model with Gaussian process priors. Statistica Sinica. online version is available now. arXiv:1605.06779
7. Zhanfeng Wang, Kai Lia, Jian Qing Shi (2019). A robust estimation for the extended t-process regression model. Statistics and Probability Letters. online version is available now.
8. 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. online version is available now.
9. 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. online version is available now.
10. 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).
11. 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.
12. Cao, C., Chen, M., Wang, Y and Shi, J. Q. (2018) Heteroscedastic replicated measurement error models under asymmetric heavy-tailed distributions. 33, 319-338. Computational Statistics, (online version).
13. 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, 12(11): e0188684. https://doi.org/10.1371/journal.pone.0188684.
14. Cao, C., Wang, Y., Shi, J. Q. and Lin, J. (2017) Measurement Error Models for Replicated Data Under Asymmetric Heavy-Tailed Distributions. Computational Economics , (online version is available now).
15. 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, (online version is available now). arXiv:1705.05618 and Supplementary Material
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., 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.
18. 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, 2715-33.
19. Wang, B. and Shi, J. Q. (2014) Generalized Gaussian process regression for non-Gaussian functional data. Journal of American Statistical Association 99, 285-298. arXiv:1401.8189
20. 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. (available online DOI:10.1080/02331888.2013.800072)
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. 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.
23. Lin, N. X., Shi, J. Q. and Henderson, R. (2012). Doubly misspecified models. Biometrika, 99, 285-298.
24. 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.
25. Yi, G., Shi, J. Q. and Choi, T. (2011) Penalized Gaussian Process Regression and Classification for High-Dimensional Nonlinear DataBiometrics. 67, 1285-1294.
26. 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. [Dr. Chootrakool's thesis]
27. 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 joint winning paper of the 2011 Best Paper Award for the Journal of Nonparametric Statistics and American Statistical Association Section on Nonparametric Statistics)
28. 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. [Full text]
29. 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.[Full text]
30. Shi, J. Q. and Tang, M. L. (2009). Trend estimation (2^nd edition). Encyclopaedia of Biopharmaceutical Statistics. URL: http://dx.doi.org/10.1081/E-EBS-120041929
31. 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.[Full text]
32. 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.
33. 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. [Full text] [Matlab codes for UNIX and WINDOWS].
34. 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.
35. Shi, J. Q. and Tang, M. L. (2007). Trend estimation. Encyclopaedia of Biopharmaceutical Statistics 1:1, 1-7 URL: http://dx.doi.org/10.1081/E-EBS-120041929(Invited contribution foran entry to the Encyclopedia of Biopharmaceutical Statistics, peer-reviewed)
36. Shi, J.Q., Murray-Smith, R. and Titterington, D.M. (2005). Hierarchical Gaussian process mixtures for regression. Statistics and Computing, 15, 31-41.
37. Kamnik, R., Shi, J.Q., Murray-Smith, R. and Bajd, T. (2000).Nonlinear modelling of FES-supported standing up in paraplegia for selection of feedback sensors. IEEE Transactions on Neural Systems & Rehabilitation Engineering, 13, 40-52.[Full text].
38. Shi, J.Q., Murray-Smith, R., Titterington, D.M. and Pearlmutter, B.A.(2005). Learnig 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.
39. Shi, J.Q. and Copas, J.B. (2004). Meta-analysis for trend estimation. Statistics in Medicine, 23, 3-19.
40. Shi, J.Q., Murray-Smith, R. and Titterington, D. M. (2003). Bayesian regression and classification using mixtures of Gaussian process. International Journal of Adaptive Control and Signal Processing. 17, 149-161.
41. Shi, J.Q. and Copas, J.B. (2002). Meta-analysis for 2x2 Tables using an average Markov chain Monte Carlo EM algorithm. J. R. Statist. Soc. B, 64, 221-236.
42. 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. 135-151. [Full text]
43. 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. [Full text]
44. 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. [Full text]
45. 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. [Data set is publicly available]
46. Copas, J.B. and Shi, J.Q. (2000). Meta-analysis, funnel plots and sensitivity analysis. Biostatistics, 1, 247-262. [Abstract / Full text]
47. 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. [Full text] [Related report]
48. 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.
49. Lee, S.Y. and Shi, J.Q. (2000). Bayesian analysis of structural equation model with fixed covariates. Structure Equation Modeling. 7, 411-430.
50. Shi, J.Q. and Wan, F.H. (1999). Diagnostics for empirical Bayes models. Systems Science and mathematical Sciences, 12, No.2, 104-114.
51. Lee, S.Y. and Shi, J.Q. (1998). Analysis of covariance structure with independent and non-identically distributed observations.Statistica Sinica, 8, 543-557.
52. 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.
53. 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.
54. 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.
55. 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.
56. Shi, J.Q. and Wei, B.C. (1995). Bayesian local influence. Mathematica Applicata, 8, No.2, 237-245.
57. 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.
58. Shi, J.Q. (1995). Assessing local prior influence in Bayesian analysis. Applied Mathematics - A journal of Chinese University, 10B, 123-132.
59. Wei, B.C. and Shi, J.Q. (1994). On statistical models for regression diagnostics. Ann. Inst. Statist. Math.,46,No. 2, 267-278.
60. Shi, J.Q. (1993). Regression transformation diagnostics in transform-both-sides model. Statistics & Probability Letters, 16, 411-20.

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