Faming Liang

Professor,  Department of Statistics,  Texas A&M University, College Station, TX 77843-3143.

Office: BLOC 406D;    Phone: (979) 8458885;    Email: fliang@stat.tamu.edu

Courses taught:

•       Computing for Statistics

•       Computer Intensive Statistical Methods

•       Linear Model

•       Regression Analysis

•       Probability

•       Mathematical Statistics

•       Introduction to Linear Models

•       Probability (graduate course)

•       Statistical Inference (graduate course)

•       Computing for Bioinformatics (graduate course)

•       Monte Carlo Methods (graduate course)
  

Lecturing Assignment for Semester II, 2012-2013:

STAT 652  Statistics in Research II

Syllabus 

•  Chapter 11: Linear Regression and Correlation (Lecture notes)
  
•  Chapter 12: Multiple Regression and the General Linear Model (Lecture notes)
  
•  Chapter 13: Further Regression Topics (Lecture notes)
•  Chapter 14: Analysis of Variance for Completely Randomized Designs (Lecture notes)
•  Chapter 15: Analysis of Variance for Blocked Designs (Lecture notes)
•  Chapter 16: The Analysis of Covariance (Lecture notes)
•  Chapter 17: Analysis of Variance for Some Fixed-, Random-, and Mixed-Effects Models (Lecture notes)
•  Chapter 18: Split-Plot, Repeated Measures, and Crossover Designs (Lecture notes) 
•  Chapter 19: Analysis of Variance for some Unbalanced Designs (Lecture notes)
  

Assignments

• Assignment 1 
• Assignment 2
• Assignment 3
• Assignment 4
• Assignment 5
• Assignment 6
• Assignment 7
  
• Assignment 8
• Assignment 9
  

Past Tests

•    Test I
•    Test II
•    Test III
  

Research Interests: 

• Markov Chain Monte Carlo

• Bioinformatics

• Spatial Statistics
  

• Neural Network

• Stochastic Optimization

• Computational Physics

Research Group 

• Students

• Group Meeting Schedule
  
  

Recent Talks

•  Monte Carlo DWIS
  

•   FDR computation

•   SAMC and its applications

•   Theory and Applications of SAMC

•  Bayesian network
  
  
  
  

Editorial Service

•  Associate Editor, Journal of Computational and Graphical Statistics, 2006-Present.

•  Associate Editor, Journal of American Statistical Association, 2010-Present

•  Associate Editor, Bayesian Analysis, 2010-Present 

•  Associate Editor, Technometrics, 2013-Present
  

•   Editorial Board Member, International Journal of Operations Research and Information Systems (IJORIS)}, 2008---present.

•   Editorial Board Member, Journal of Advanced Research in Statistics and Probability, 2009--present

•   Associate editor, Biometrics, 2006-2008.

Honors

•   IMS fellow, 2013.

•   ASA fellow, 2011.

•   Elected member, International Statistical Institute (ISI), 2005.

•   Statistica Sinica, invited paper, 2001 Joint Statistical Meeting.

Selected Publications:

• Books

1.  Kendall, W.S., Liang, F., and Wang, J.S. (2005) (Editors) Markov Chain Monte Carlo: Innovations and Applications. World Scientific: Singapore.  ISBN 981-256-427-6.

                                                               
      
      2.   Liang, F., Liu, C. and Carroll, R.J. Advanced Markov chain Monte Carlo: Learning from Past Samples.  Wiley.  ISBN: 978-0-470-74826-8.

•        Software   

1.       Liang, F., Wu, M., and Tian, Y. (2008)  Bayesian LatentChIP.

2.       Wu, M. and Liang, F. (2009)  Testing multiple hypotheses using population information of samples. Code ,  Supplementary Material.
       

•        Articles

1. Wong, W.H. and Liang, F. (1997) Dynamic weighting in Monte Carlo and optimization, Proc. Natl. Acad. Sci. USA, 94, 14220-14224.

2. Liang, F. and Wong, W.H. (1999) Dynamic weighting in simulations of spin systems, Phys. Lett. A, 252, 257-262.

3. Cong, J., Kong, T., Xu, D., Liang, F., Liu, J.S., and Wong, W.H. (1999) Relaxed simulated tempering for VLSI floorplan designs, Proc. Asia and South Pacific Design Automation Conf., Hong Kong, pp13-16.

4. Cong, J., Kong, T., Xu, D., Liang, F., Liu, J.S., and Wong, W.H. (2000) Dynamic weighting Monte Carlo for constrained floorplan design in mixed signal application,Proc. Asia and South Pacific Design Automation Conf., Japan.

5. Sanderson P, Taylor D., Ali M., Liew S.C., Couturier S., Lee G., Truong Y., Liang F., Gin K. and Holden H. (1999) Development of a methodology for monitoring variations in turbid waters draining modified wetlands in southeast Sumatra, Indonesia: preliminary results for suspended sediments, Eighth International Symposium on the Interactions Between Sediments and Water}, Beijing, pp. 13-17.

6. Liu, J.S., Liang, F., and Wong. W.H. (2000) The use of multiple-try method and local optimization in Metropolis sampling, J. Amer. Statist. Assoc.,95, 121-134.

7. Liang, F. and Wong. W.H. (2000) Evolutionary Monte Carlo sampling: applications to $C_p$ model sampling and change-point problem. Statistica Sinica,10, 317-342.

8. Truong, Y. K., Liang, F., Sanderson, P. G., Taylor, D. and Liew, S. C. (2000) M onitoring variations in turbid waters draining modified wetlands in southeast Su matra, Indonesia: A functional data analytic approach. In Nonparametric approach to Knowledge Discovery, Nara, Japan, December 14-17, 2000. Proceedings.

9. Liu, J.S., Liang, F., and Wong. W.H. (2001) A theory for dynamic weighting in Monte Carlo, J. Amer. Statist. Assoc., 96, 561-573.

10. Liang, F. and Wong. W.H. (2001) Real-parameter evolutionary sampling with applications in Bayesian Mixture Models, J. Amer. Statist. Assoc., 96, 653-666. 

11. Liang, F., Truong, Y.K. and Wong, W.H. (2001) Automatic Bayesian model averaging for linear regression and applications in Bayesian curve fitting. Statistica Sinica , 11, 1005-1029. 

12. Liang, F. and Wong, W.H. (2001) Evolutionary Monte Carlo for Protein Folding simulations, Journal of Chemical Physics , 115, 3374-3380.

13. Liang, F. (2002) Some connections between Bayesian and non-Bayesian methods for regression model selection.   Statistics & Probability Letters , 57, 53-63.

14. Liang, F. (2002) Dynamically Weighted Importance Sampling in Monte Carlo Computation, J. Amer. Statist. Assoc. , 97, 807-821.

15. Liang, F. (2003) An Effective Bayesian Neural Network Classifier with a Comparison Study to Support Vector Machine,  Neural Computation, 15, 1959-1989.

16.  Liang, F. (2003) Use of sequential structure in simulation from high dimensional systems, Physical Review E,  67, 56101-56107.

17. Zhang, J., Liang, F., Dassen, W. and de Gunst, M. (2003)  Search for Haplotype-Interactions that are susceptible to type I diabetes using unphased genotype data,  American J. Human Genetics, 73,   1385-1401.
    

18. Liang, F. (2004). Generalized 1/k-Ensemble Algorithm, Physical Review E, 69, 66701-66707.

19.  Liang, F. (2004) Annealing Contour Monte Carlo for Structure Optimization in an Off-lattice Protein Model.  Journal of Chemical Physics, 120, 6756-6763. (This paper is selected  by editors expert for re-publication in the April 1, 2004 issue of  Virtual Journal of Biological Physcis Research.)

20.  Liang, F. (2004) Annealing contour Monte Carlo for neural network training. Proceedings on Cybernetics and Informatics Technologies, Systems and Applications, Volume III, pp.130-135.

21. Liang, F. and Kuk, Y.C.A. (2004)  A finite population estimation study with Bayesian neural networks.  Survey Methodology,  30, 219-234.

22. Liang, F. (2005) Bayesian neural networks for non-linear time series forecasting.  Statistics and Computing, 15, 13-29.

23.  Liang, F. and Liu, C. (2005)  Efficient MCMC estimation of discrete distributions.  Computational Statistics and Data Analysis,  49, 1039-1052.

24. Liang, F. (2005) Evidence Evaluation for Bayesian Neural Networks. Neural Computation,  17, 1385-1410.

25. Liang, F. (2005) Generalized Wang-Landau algorithm for Monte Carlo Computation. J. Amer. Statist. Assoc., 100, 1311-1327.

26. Liang, F. (2005) Determination of normalizing constants for simulated tempering. Physica A, 356, 468-480.

27. Liang, F. (2005). Annotated bibliography: Advanced Markov chain Monte Carlo methods.  ISBA Bulletin,  12(4),  2-5.

28. Liang, F. and Huang, J. (2006) Book  Review: Statistical and Computational Inverse Problems.  Technometrics, 48, 146.

29. Liang, F. (2006) A theory on flat histogram Monte Carlo algorothms. Journal of Statistical Physics, 122,  511-529.

30. Zhu, H., Liang, F., Gu, M. and Peterson, B. (2006) Stochastic Approximation algorithms for estimation of spatial mixed models. In  Handbook of Computing and Statistics with Applications, Vol.   1  (eds. S.Y. Lee), Elsevier. pp.399-421.

31. Liang, F., Liu, C. and Wang, N. (2007) A sequentail  Bayesian procedure for identification of differentially expressed genes.  Statistica Sinica, 12, 571-597.

32. Liang, F., Liu, C. and Carroll, R.J. (2007) Stochastic Approximation in Monte Carlo Computation.   J. Amer. Statist. Assoc., 102, 305-320.

33. Liang, F. and Wang, N. (2007) Dynamic Hierarchical Clustering of Gene Expression Profiles.  Pattern Recognition Letters,  28,  1062-1076.

34. Liang, F. (2007) Use of SVD-based probit transformation in clustering gene expression profiles.  Computational Statistics and Data Analysis,  51, 6355-6366.

35. Liang, F. (2007) Continuous Contour Monte Carlo for Marginal Density Estimation with an Application
    to a Spatial Statistical Model, Journal of Computational and Graphical Statistics, 16(3), 608-632.

36. Liang, F.  (2007)  Annealing Stochastic Approximation Monte Carlo for Neural Network Training.  Machine Learning, 68(3), 201-233.

37.  Cheon, S. and Liang, F. (2008) Phylogenetic Tree Reconstruction Using Sequential Stochastic Approximation   Monte Carlo.  BioSystems, 91, 94-107.

38. Liang, F. (2008) Stochastic Approximation Monte Carlo for MLP Learning.  In Encyclopedia of Artificial Intelligence, (eds. J.R.R. Dopico,  J.D. de la Calle, and A.P. Sierra), pp.1482-1489.

39. Zhang, J., J Rggieli, M. Schipper, M. Entius, F. Liang, J. Koerselman, H Ruven, Y van der Graaf, D. Grobbee, and P. Doevendans (2008)  Inflammatory gene haplotype-interaction networks involved in coronary collateral formation. Human Heredity, 66, 252-264.

40. Liang, F. and Zhang, J. (2008) Estimating FDR under general dependence using stochastic approximation.  Biometrika,  95(4), 961-977.

41.  Liang, F. (2008) Clustering gene expression profiles using mixture model ensemble averaging approach.  JP Journal of Biostatistics, 2(1), 57-80.

42. Zhang, J. and Liang, F. (2008) Convergence of stochastic approximation under irregular conditions. StatisticaNeerlandica, 62, 393-403.

43. Yuan, R., Ding, Y., and Liang, F. (2008)  Adaptive Evolutionary Monte Carlo for Optimizations with Applications to Sensor Placement Problems.  Statistics and Computing, 18, 375-390.

44. Liang, F. (2009) Improving SAMC Using Smoothing Methods: Theory and Applications to Bayesian Model Selection Problems. The Annals of Statistics,  37, 2626-2654.

45. Liang, F. (2009) On the use of SAMC for Monte Carlo integration. Statistics & Probability Letters, 79, 581-587.

46.  Liang, F. and Zhang, J. (2009) Learning Bayesian Networks for Discrete Data. Computational Statistics & Data Analysis, 53, 865-876.

47. Zhang, X.S., Liang, F., Srinivasan, R., and Van Liew, M. (2009) Estimating uncertainty of streamflow simulation using Bayesian neural networks.  Water Resources Research, 45, W02403.

48.  Cheon, S. and Liang, F. (2009) Bayesian phylogeny analysis via stochastic approximation Monte Carlo.  Molecular Phylogenetic & Evolution, 53, 394-403.

49. Wu, M., Liang, F. and Tian, Y. (2009) Bayesian Modeling of ChIP-chip Data Using Latent Variables. BMCBioinformatics, 10: 352.

50. Xie, Y., Zhang, Y., and Liang, F. (2009) Crash injury severity analysis using Bayesian ordered probit models.  Journal of Transportation Engineering,  135(1), 18-25.

51.  Liang, F. (2009) Learning Bayesian Networks for Gene Expression Data. In Bayesian Modeling in Bioinformatics (Eds. D.K. Dey, S. Ghosh  and B.K. Mallick), pp.349-367.

52. Liang, F. and Cheon, S. (2009) Monte Carlo dynamically weighted importance sampling for spatial models with intractable normalizing constants. Journal of Physics: Conference Series, 197, 012004.
    

53. Zhang, P., Hill, C., Xia, Y., and Liang, F. (2010)   Modeling the relationship between EDI implementation
    and firm performance improvement with neural networks.  IEEE Transactions on Automation Science and Engineering, 7, 96-110.

54. Liang, F. (2010) A double Metropolis-Hastings sampler for spatial models with intractable normalizing constants. Journal of Statistical Computing and Simulation, 80, 1007-1022.

55. Martinez, J.N., Liang, F., Zhou, L., and Carroll, R.J. (2010)   Longitudinal Functional Principal Component Modeling via Stochastic Approximation Monte Carlo. Canadian Journal of Statistics, 38, 256-270.
    

56. Mo, Q. and Liang, F. (2010)  Bayesian modeling of ChIP-chip data through a high-order Ising model.  Biometrics, 66, 1284-1294.
    

57. Zhang, J. and Liang, F. (2010) Exponential power mixture models for clustering. Biometrics, 66, 1078-1086.
    

58. Mo, Q. and Liang, F. (2010) A hidden Ising model for ChIP-chip data analysis. Bioinformatics, 26, 777-783.
    

59. Liang, F. (2010) Trajectory averaging for stochastic approximation  MCMC algorithms. Annals of Statistics, 38, 2823-2856.
    

60. Wu, M. and Liang, F. (2010) Testing Multuiple Hypotheses  Using Population Information of Samples.  JPJournal of Biostatistics, 4, 181-201.
    

61. Liang, F. (2011) Evolutionary Stochastic Approximation Monte Carlo for Global Optimization.  Statistics and Computing, 21, 375-393.
    

62. Yin, G., Ma, Y., Liang, F. and Yuan, Y. (2011) Stochastic generalized method of moments. Journal of Computational and Graphical Statistics, 20, 714-727.
    

63. Yu, K., Liang, F., Chatterjee, N., and Ciampa, J. (2011)  Efficient p-Value Evaluation for Resampling-based tests. Biostatistics, 12, 582-593.
    

64. Zhang, N., Li, X., Tao k., Jiang, L., Ma, T.,  Yan, S., Yuan, C. Moran, M.S., Liang, F., Haffty, B.G. and Yang, Q. (2011) BCL-2 (-938C>A) polymorphism is associated with breast cancer susceptibility.  BMC Medical Genetics, 12:48.

65.  Cheon, S. and Liang, F. (2011) Folding small proteins via annealing stochastic approximatio Monte Carlo. BioSystems, 105, 243-249.

66. Zhang, X., Liang, F., Yu, B. and Zong, Z. (2011) Explicitly integrating parameter, input and  structure uncertainties into Bayesian neural networks for probabilistic hydrologic forecasting.  Journal of Hydrology, 409, 696-709.
    

67. Park, J. and Liang, F. (2012) Bayesian analysis of geostatistical models with an auxiliary lattice. Journal of computational and Graphical Statistics, 21, 453-475.
    

68. Shi, X., Zhu, H., Ibrahim, J.G., Liang, F., Lieberman, J. and Styner, M. (2012)  Intrinsic regression models for medial representation of subcortical structures. J. Amer. Statist. Assoc., 107, 12-23.
    

69. Yu, K., Wacholder, S., Wheeler, W., Wang, Z., Caporaso, N., Landi, M.T., Liang, F.  (2012) A flexible Bayesian model for studying gene-environment interaction.  PLoS Genetics,  8(1): e1002482.

70. Jin, I.K. and Liang, F. (2012) Fitting social network models using varying truncation stochastic approximation MCMC algorithm. Journal of Computational and Graphical Statistics, in press.

71. Jin, I.K. and Liang, F. (2012) Bayesian SAMC for distributions with intractable normalizing constants,  Computational Statistics & Data Analysis, in press.

72.  Ryu, D., Liang, F. and Mallick, B.K. (2013). Sea Surface Temperature Modeling using Radial Basis Function Networks with a Dynamically Weighted Particle Filter.  J. Amer. Statist. Assoc., 108, 111-123.
    

73. Liang, F., Cheng, Y., Song, Q., Park, J., and Yang, P. (2013). A Resampling-based Stochastic Approximation Method for Analysis of Large Geostatistical Data. J. Amer. Statist. Assoc., 108, 325-339.
    

74. Zhou, C., Yang, P., Dessler, A.E., and Liang, F. (2012). Statistic of horizontally oriented ice cloud  crystals in optically thick clouds. IEEE Geoscience and Remote Sensing Letters, in press.

75. Liang, F., Song, Q., and Yu, K. (2013).  Bayesian Subset Modeling for High Dimensional Generalized Linear Models.  J. Amer. Statist. Assoc., in press.

76. Liang, F. and Jin, I.K. (2013). A Monte Carlo Metropolis-Hastings Algorithm for Sampling from Distributions with Intractable Normalizing Constants. Neural Computation, in press.

77. Cheon, S., Liang, F., Chen, Y., and Yu, K. (2013).  Stochastic Approximation Monte Carlo Importance Sampling for Approximating Exact Conditional Probabilities. Statistics and Computing, in press.

78. Park, J. and Liang, F. (2013). A prediction-oriented Bayesian site selection approach for large spatial data. Journal of Statistical Research, in press.