Selected Publications

Book

· 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 

• Choi, T., Shi, J. Q. and Wang, B. (2011). Statistical inference for Gaussian process partially linear regression models.
• Wang, B. and Shi, J. Q. (2011) Generalized Gaussian Process Regression for non-Gaussian Functional Data.

Selected published papers (peer-reviewed)

• Lin, N. X., Shi, J. Q. and Henderson, R. (2012). Doubly misspecified models. Biometrika, 99, 285-298.
• 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.

§ Yi, G., Shi, J. Q. and Choi, T. (2011) Penalized Gaussian Process Regression and Classification for High-Dimensional Nonlinear Data. Biometrics. 67, 1285-1294. DOI: 10.1111/j.1541-0420.2011.01576.x.

• 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]
• 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 for the JNPS 2011 Best Paper Award)
• 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]
• Lee, W., Shi, J. Q. and Lee, Y. (2010). Approximate Conditional Inference in Mixed Effects Models with Binary Data. [Full text]. Computational Statistics and Data Analysis. 54, 173-184.
• Shi, J. Q. and Tang, M. L. (2009). Trend estimation (2^nd edition). Encyclopaedia of Biopharmaceutical Statistics, 1:1, 1 – 7 URL: http://dx.doi.org/10.1081/E-EBS-120041929 .
• 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]
• 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.

§ Shi, J.Q., Wang, B., Murray-Smith, R. and Titterington, D.M. (2007). Gaussian process functional regression modeling for batch data. [Full text] [Matlab codes for UNIX and WINDOWS]. Biometrics, 63, 714-723.

§ 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 .

§ 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 for an entry to the Encyclopedia of Biopharmaceutical Statistics, peer-reviewed)

§ Shi, J.Q., Murray-Smith, R. and Titterington, D.M. (2005). Hierarchical Gaussian process mixtures for regression. Statistics and Computing 15, 31-41.

§ 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 [Full text]. 

§ 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.

§ Shi, J.Q. and Copas, J.B. (2004). Meta-analysis for trend estimation. Statistics in Medicine 23, 3-19.

§ 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.

§ 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.

§ 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]

§ 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]

§ 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]

§ 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]

§ Copas, J.B. and Shi, J.Q. (2000). Meta-analysis, funnel plots and sensitivity analysis. Biostatistics, 1, 247-262. [Abstract / Full text]

§ 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]

§ 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.

§ Lee, S.Y. and Shi, J.Q. (2000). Bayesian analysis of structural equation model with fixed covariates. Structure Equation Modeling. 7, 411-430.

§ Shi, J.Q. and Wan, F.H. (1999). Diagnostics for empirical Bayes models. Systems Science and mathematical Sciences, 12, No.2, 104-114. 

§ Lee, S.Y. and Shi, J.Q. (1998). Analysis of covariance structure with independent and non-identically distributed observations.Statistica Sinica, 8, 543-557.

§ 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.

§ 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.

§ 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.

§ 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.

§ Shi, J.Q. and Wei, B.C. (1995). Bayesian local influence. Mathematica Applicata, 8, No.2, 237-245.

§ 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.

§ Shi, J.Q. (1995). Assessing local prior influence in Bayesian analysis. Applied Mathematics - A journal of Chinese University, 10B, 123-132.

§ Wei, B.C. and Shi, J.Q. (1994). On statistical models for regression diagnostics. Ann. Inst. Statist. Math.,46,No. 2, 267-278.

§ Shi, J.Q. (1993). Regression transformation diagnostics in transform-both-sides model. Statistics & Probability Letters, 16, 411-20.

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