HKU HKU Dept of Statistics & Actuarial Science, HKU
 
 
Gary G.L. TIAN

Associate Professor

BSc(Hunan Normal); MSc(Wuhan); PhD(Chinese Acad of Sc)

Office:

Rm 224, Run Run Shaw Building

Email:

gltian@hku.hk

 
Phone:

(852) 3917-1984

Fax:

(852) 2858-9041

 
   Garyˇ¦s Updated CV (Updated on September 4, 2014)

   Monographs

  1. Tan MT, Tian GL and Ng KW (2010). Bayesian Missing Data Problems: EM, Data Augmentation and Non-iterative Computation. Chapman & Hall/CRC (Biostatistics Series), Boca Raton, FL 33487-2742, USA. 328 pages. [R Codes]

  2. Ng KW, Tian GL and Tang ML (2011). Dirichlet and Related Distributions: Theory, Methods and Applications. John Wiley & Sons Ltd (Wiley Series in Probability and Statistics), Chichester, West Sussex, United Kingdom. 310 pages.

  3. Tian GL and Tang ML (2014). Incomplete Categorical Data Design: Non-randomized Response Techniques for Sensitive Questions in Surveys. Chapman & Hall/CRC (Statistics in the Social and Behavioral Sciences), Boca Raton, FL 33487-2742, USA. 301 pages. [R Codes]

   Published Research Papers in Peer-Reviewed International Journals

Area 1: Multivariate zero-inflated count data analysis (1 paper, Current Research Interest)

  1. Zhang C, Tian GL and Ng KW (2014). Properties of the zero-and-one-inflated Poisson distribution and likelihood-based inference methods. Statistics and Its Interface, in press.

Area 2: Incomplete categorical data and missing data analysis (15 papers, Current Research Interest)

  1. Pei YB, Tian GL and Tang ML (2014). Testing homogeneity of proportion ratios for stratified correlated bilateral data in two-arm randomize clinical trials. Statistics in Medicine, in press.

  2. Li HQ, Chan ISF, Tang ML, Tian GL and Tang NS (2014). Confidence-interval construction for rate ratio in matched-pair studies with incomplete data. Journal of Biopharmaceutical Statistics 24(3), 546-568.

  3. Tang ML, He XJ and Tian GL (2013). A confidence interval approach for comparative studies involving binary outcomes in paired organs. Communication in Statistics: Simulation and Computation 42, 425ˇV453.

  4. Tian GL, Tang ML and Liu CL (2012). Accelerating the quadratic lower-bound algorithm via optimizing the shrinkage parameter. Computational Statistics and Data Analysis 56(2), 255-265.

  5. Tang ML, Li HQ, Chan ISF and Tian GL (2011). On confidence interval construction for establishing equivalence of two binary-outcome treatments in matched-pair studies in the presence of incomplete data. Statistics in Biosciences 3(2), 223-249.

  6. Tang ML, Ling MH, Ling L and Tian GL (2010). Confidence intervals for a difference between proportions based on paired data. Statistics in Medicine 29(1), 86-96.

  7. Tian GL, Tang ML, Yuen KC and Ng KW (2010). Further properties and new applications for the nested Dirichlet distribution. Computational Statistics and Data Analysis 54, 394-405.

  8. Tang ML, Ling MH and Tian GL (2009). Exact and approximate unconditional confidence intervals for proportion difference in the presence of incomplete data. Statistics in Medicine 28, 625-641.

  9. Ng KW, Tang ML, Tian GL and Tan M (2009). The nested Dirichlet distribution and incomplete categorical data analysis. Statistica Sinica 19(1), 251-271.

  10. Ng KW, Tang ML, Tan M and Tian GL (2008). Grouped Dirichlet distribution: A new tool for incomplete categorical data analysis. Journal of Multivariate Analysis 99(3), 490-509.

  11. Tang ML, Ng KW, Tian GL and Tan M (2007). On improved EM algorithm and confidence interval construction for incomplete r x c tables. Computational Statistics and Data Analysis 51(6), 2919-2933.

  12. Tan M, Fang HB, Tian GL and Wei G (2005). Testing multivariate normality in incomplete data of small sample size. Journal of Multivariate Analysis 93, 164-179.

  13. Tian GL, Ng KW and Geng Z (2003). Bayesian computation for contingency tables with incomplete cell-counts. Statistica Sinica 13(1), 189-206.

  14. Tan M, Fang HB, Tian GL and Houghton PJ (2002). Small-sample inference for incomplete longitudinal data with truncation and censoring in tumor xenograft models. Biometrics 58(3), 612-620.

  15. Fang KT, Geng Z and Tian GL (2000). Statistical inference for truncated Dirichlet distribution and its application in misclassification. Biometrical Journal 42(8), 1053-1068.

Area 3: Constrained parameter models and variable selection (17 papers)

  1. Wu LC, Zhang ZZ, Tian GL and Xu DK (2014). A robust variable selection to t-type joint generalized linear models via penalized t-type pseudo-likelihood. Communications in Statistics - Simulation and Computation, in press.

  2. Wu LC, Tian GL and Ma T (2014). Variable selection in joint location, scale and skewness models with a skew-t-normal distribution. Computational Statistics and Data Analysis, in press.

  3. Wang MQ, Song LX and Tian GL (2014). SCAD-penalized least absolute deviation regression in high dimensional models. Communication in Statistics: Theory and Methods, in press.

  4. Tian GL, Wang MQ and Song LX (2014). Variable selection in the high-dimensional continuous generalized linear model with current status data. Journal of Applied Statistics, 41(3), 467-483.

  5. Fang HB, Deng DL, Tian GL, Shen LX, Duan KM and Song JZ (2012). Analysis for temporal gene expressions under multiple biological conditions. Statistics in Biosciences 4(2), 282-299.

  6. Zheng SR, Guo JH, Shi NZ and Tian GL (2012). Likelihood-based approaches for multivariate linear models under inequality restrictions for incomplete data. Journal of Statistical Planning and Inferences 142, 2926-2942.

  7. Tian GL, Ng KW and Yu PLH (2011). A note on the binomial model with simplex constraints. Computational Statistics and Data Analysis 55(12), 3381-3385.

  8. Gao W, Shi NZ, Tang ML, Fu LY and Tian GL (2010). Unified generalized iterative scaling and its applications. Computational Statistics and Data Analysis 54, 1066-1078.

  9. Liu ZQ, Chen DC, Tian GL, Tang ML, Tan M and Sheng L (2010). Efficient support vector machine method for survival prediction with SEER data. In Advances in Computational Biology (H.R. Arabnia, ed.), 11-18. Springer (Advances in Experimental Medicine and Biology 680), New York.

  10. Tian GL, Fang HB, Liu ZQ and Tan M (2009). Regularized (bridge) logistic regression for variable selection based on ROC criterion. Statistics and Its Interface, 2, 493-502.

  11. Tian GL, Ng KW and Tan M (2008). EM-type algorithms for computing restricted MLEs in multivariate normal distributions and multivariate t-distributions. Computational Statistics and Data Analysis, 52(10), 4768-4778.

  12. Tian GL, Tang ML, Fang HB and Tan M (2008). Efficient methods for estimating constrained parameters with applications to regularized (lasso) logistic regression. Computational Statistics and Data Analysis 52(7), 3528-3542.

  13. Tan M, Tian GL, Fang HB and Ng KW (2007). A fast EM algorithm for quadratic optimization subject to convex constraints. Statistica Sinica 17(3), 945-964.

  14. Liu ZQ, Jiang F, Tian GL, Wang S, Sato F, Meltzer SJ and Tan M (2007). Sparse logistic regression with L_p penalty for biomarker identification. Statistical Applications in Genetics and Molecular Biology 6(1), Article 6.

  15. Fang HB, Tian GL, Xiong XP and Tan M (2006). A multivariate random-effects model with restricted parameters: Application to assessing radiation therapy for brain tumors. Statistics in Medicine 25(11), 1948-1959.

  16. Tan M, Fang HB, Tian GL and Houghton PJ (2005). Repeated-measures models with constrained parameters for incomplete data in tumor xenograft experiments. Statistics in Medicine 24(1), 109-119.

  17. Tan M, Tian GL and Fang HB (2003). Estimating restricted normal means using the EM-type algorithms and IBF sampling. In Development of Modern Statistics and Related Topics - In Celebration of Professor Yaoting Zhang's 70th Birthday (J. Huang and H. Zhang, eds.), 53-73. World Scientific Publishing Co. Inc., New Jersey.

Area 4: Sample surveys with sensitive questions (12 papers)

  1. Tian GL (2014). A new non-randomized response model: The parallel model. Statistica Neerlandica, in press.

  2. Tang ML, Wu Q, Tian GL and Guo JH (2014). Two-sample non-randomized response techniques for sensitive questions. Communication in Statistics: Theory and Methods, 43(2), 408-425.

  3. Liu Y and Tian GL (2014). Sample size determination for the parallel model in a survey with sensitive questions. Journal of the Korean Statistical Society 43, 235-249.

  4. Liu Y and Tian GL (2013b). A variant of the parallel model for sample surveys with sensitive characteristics. Computational Statistics and Data Analysis 67, 115-135.

  5. Liu Y and Tian GL (2013a). Multi-category parallel models in the design of surveys with sensitive questions. Statistics and Its Interface 6(1), 137-149.

  6. Yu JW, Lu Y and Tian GL (2013). A survey design for a sensitive binary variable correlated with another non-sensitive binary variable. Journal of Probability and Statistics, Volume 2013, Article ID 827048, 11 pages, http://dx.doi.org/10.1155/2013/827048.

  7. Tian GL, Tang ML, Liu ZQ, Tan M and Tang NS (2011). Sample size determination for the non-randomized triangular model for sensitive questions in a survey. Statistical Methods in Medical Research 20(3), 159-173.

  8. Tang ML, Tian GL, Tang NS and Liu ZQ (2009). A new non-randomized multi-category response model for surveys with a single sensitive question: Design and analysis. Journal of the Korean Statistical Society 38, 339-349.

  9. Tan M, Tian GL and Tang ML (2009). Sample surveys with sensitive questions: A non-randomized response approach. The American Statistician 63(1), 9-16.

  10. Tian GL, Yuen KC, Tang ML and Tan M (2009). Bayesian non-randomized response models for surveys with sensitive questions. Statistics and Its Interface 2, 13-25.

  11. Yu JW, Tian GL and Tang ML (2008). Two new models for survey sampling with sensitive characteristic: Design and analysis. Metrika 67(3), 251-263.

  12. Tian GL, Yu JW, Tang ML and Geng Z (2007). A new non-randomized model for analyzing sensitive questions with binary outcomes. Statistics in Medicine 26(23), 4238-4252.

Area 5: Reliability and prediction inferences (3 papers)

  1. Tian GL, Tang ML and Yu JW (2011). Bayesian estimation and prediction for the power law process with left-truncated data. Journal of Data Science 9(3), 445-470.

  2. Yu JW, Tian GL and Tang ML (2008). Statistical inference and prediction for the Weibull process with incomplete observations. Computational Statistics and Data Analysis 52(3), 1587-1603.

  3. Yu JW, Tian GL and Tang ML (2007). Predictive analyses for non-homogeneous Poisson processes with power law using Bayesian approach. Computational Statistics and Data Analysis 51(9), 4254-4268.

Area 6: Experimental design for drug combination studies (6 papers)

  1. Tan M, Fang HB and Tian GL (2009). Dose and sample size determination for multi-drug combination studies. Statistics in Biopharmaceutical Research 1(3), 301-316.

  2. Fang HB, Tian GL, Li W and Tan M (2009). Design and sample size for evaluating combinations of drugs of linear and log-linear dose-response curves. Journal of Biopharmaceutical Statistics 19(4), 625-640.

  3. Tian GL, Fang HB, Tan M, Qin H and Tang ML (2009). Uniform distributions in a class of convex polyhedrons with applications to drug combination studies. Journal of Multivariate Analysis 100(8), 1854-1865.

  4. Tan M, Fang HB and Tian GL (2005). Statistical analysis for tumor xenograft experiments in drug development. In Contemporary Multivariate Analysis and Experimental Designs - In Celebration of Professor Kai-Tai Fang's 65th Birthday (J. Fan and G. Li, eds.), 351-368. World Scientific Publishing Co. Inc., New Jersey.

  5. Fang HB, Tian GL and Tan M (2004). Hierarchical models for tumor xenograft experiments in drug development. Journal of Biopharmaceutical Statistics 14(14), 931-945.

  6. Tan M, Fang HB, Tian GL and Houghton PJ (2003). Experimental design and sample size determination for testing synergism in drug combination studies based on uniform measures. Statistics in Medicine 22(13), 2091-2100.

Area 7: Cancer clinical trial and design (3 papers)

  1. Lin W, Voskens CJ, Zhang XY, Schindler DG, Wood A, Burch E, Wei YD, Chen LP, Tian GL, Tamada K, Wang LX, Schulze DH, Mann D, Strome SE (2008). Fc dependent expression of CD137 on human NK cells: insights into ˇ§agonisticˇ¨ effects of anti-CD137 monoclonal antibodies. Blood 112(3), 699-707.

  2. Khanna N, Mishra SI, Tian GL, Tan M, Arnold S, Lee C, Ramachandran S, Bell L, Baquet C and Lorincz A (2007). Human papillomavirus detection in self-collected vaginal specimens and matched clinician-collected cervical specimens. International Journal of Gynecological Cancer 17, 615-622.

  3. Williams R, Mackert P, Fletcher L, Olivi S, Tian GL and Wang W (2002). Comparison of energy prediction equations with measured resting energy expenditure in children with sickle cell anemia. Journal of the American Dietetic Association 102(7), 956-961.

Area 8: Non-iterative Bayesian method (7 papers)

  1. Tian GL, Ng KW, Li KC, and Tan M (2009). Non-iterative sampling-based Bayesian methods for identifying changepoints in the sequence of cases of haemolytic uraemic syndrome. Computational Statistics and Data Analysis 53(9), 3314-3323.

  2. Tian GL, Tan M and Ng KW (2007). An exact non-iterative sampling procedure for discrete missing data problems. Statistica Neerlandica 61(2), 232-242.

  3. Tan M, Tian GL and Fang HB (2007). An Efficient MCEM algorithm for fitting generalized linear mixed models for correlated binary data. Journal of Statistical Computation and Simulation 77(11), 929-943.

  4. Tan M, Tian GL and Ng KW (2006). Hierarchical models for repeated binary data using the IBF sampler. Computational Statistics and Data Analysis 50(5), 1272-1286.

  5. Tan M, Tian GL and Ng KW (2003). A non-iterative sampling method for computing posteriors in the structure of EM-type algorithms. Statistica Sinica 13(3), 625-639.

  6. Tian GL and Tan M (2003). Exact statistical solutions using the inverse Bayes formulae. Statistics & Probability Letters 62(3), 305-315.

  7. Tan M, Tian GL and Xiong XP (2001). Explicit Bayesian solution for incomplete pre-post test problems using inverse Bayes formulae. Communications in Statistics - Theory and Methods 30(6), 1111-1129.

Area 9: Multivariate analysis (8 papers)

  1. Liu BS, Xu L, Zheng SR and Tian GL (2014). A new test for the proportionality of two large-dimensional covariance matrices. Journal of Multivariate Analysis, in press.

  2. Yin ZH, Gao W, Tang ML and Tian GL (2013). Estimation of nonparametric regression models with a mixture of Berkson and classical errors. Statistics and Probability Letters 83, 1151-1162.

  3. Yu JW and Tian GL (2011). Efficient algorithms for generating truncated multivariate normal distributions. Acta Mathematica Applicatae Sinica (English Series) 27(4), 601-612.

  4. Tian GL, Tan M, Ng KW and Tang ML (2009). A unified method for checking compatibility and uniqueness for finite discrete conditional distributions. Communication in Statistics: Theory and Methods 38(1), 115-129.

  5. Ng KW and Tian GL (2001). Characteristic functions of L1-spherical and L1-norm symmetric distributions and their applications. Journal of Multivariate Analysis 76(2), 192-213.

  6. Fang KT, Tian GL and Xie MY (1999). Uniform design over a convex polyhedron. Chinese Science Bulletin 44(2), 112-114.

  7. Tian GL and Fang KT (1999). Uniform designs for mixture-amount experiments and for mixture experiments under order restrictions. Science in China Series A: Mathematics 42(5), 456-470.

  8. Tian GL (1998). The comparison between polynomial regression and orthogonal polynomial regression. Statistics & Probability Letters 38, 289-294.