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Ph.D. (1995)
Institute of Systems Science, Chinese Academy of Sciences

Guohua Zou

Research Assistant Professor
 

 

Contact Information:

University of Rochester
Dept of Biostatistics and Computational Biology
601 Elmwood Avenue Box 630
Rochester, New York 14642
 
Office: Lattimore Medical Bldg 125
Phone: (585) 241-0725
Fax: (585) 256-2541
E-mail: Guohua_Zou@urmc.rochester.edu
 

Research Interests

I am interested in developing statistical theory and methods to analyze the practical medical, genetic and economic data. My research fields include statistical model selection, statistical genetics, econometrics, survey sampling and statistical decision theory. Special focuses are on mixed-effects models, linkage and association studies between diseases and genes, pre-test estimation and sensitivity of econometric estimators and tests, design and data analysis in surveys, and the optimality of estimators such as admissibility and minimaxity.


Selected References

  • Liang H. and Zou G.H. (2008). Improved AIC selection strategy for survival analysis. Computational Statistics and Data Analysis, in press.
  • Zuo Y.J.1, Zou G.H.1, Wang J.X., Zhao H.Y. and Liang H. (2008). Optimal two-stage design for case-control association analysis incorporating genotyping errors. Annals of Human Genetics, in press.  (1Co-first author)
  • Liang H. and Zou G.H. (2007). Analysis of relation between virologic responses and immunologic responses, patient's factors in AIDS clinical trials using a semiparametric mixed-effects model. Biometrical Journal 49 (3): 406-415.
  • Wan A.T.K., Zou G.H. and Qin H.Z. (2007). On the sensitivity of restricted least squares estimators to covariance mis-specification. Econometrics Journal 10 (3): 471-487.
  • Wan A.T.K., Zou G.H. and Ohtani K. (2006). Further results on optimal critical values of pre-test when estimating the regression error variance. Econometrics Journal 9 (1): 159-176.
  • Wu X.Y., Zou G.H. and Chen J.W. (2006). Unbiased invariant minimum norm estimation in generalized growth curve model. Journal of Multivariate Analysis 97 (8): 1718-1741.
  • Zou G.H. and Zuo Y.J. (2006). On the sample size requirement in genetic association tests when the proportion of false positives is controlled. Genetics 172 (1): 687-691.
  • Zuo Y.J., Zou G.H. and Zhao H.Y. (2006). Two-stage designs in case-control association analysis. Genetics 173 (3): 1747-1760.
  • Wan A.T.K., Zou G.H. and Ohtani K. (2006). Further results on optimal critical values of pre-test when estimating the regression error variance. Econometrics Journal 9 (1): 159-176.
  • Zou G.H. and Zuo Y.J. (2006). On the sample size requirement in genetic association tests when the proportion of false positives is controlled. Genetics 172 (1): 687-691.
  • Zou G.H. and Zhao H.Y. (2005). Family-based association tests for different family structures using pooled DNA. Annals of Human Genetics 69 (4): 429-442.
  • Zou G.H. and Zhao H.Y. (2004). The estimation of sibling genetic risk parameters revisited. Genetic Epidemiology 26 (4): 286-293.
  • Zou G.H. and Zhao H.Y. (2004). The impacts of errors in individual genotyping and DNA pooling on association studies. Genetic Epidemiology 26 (1): 1-10.
  • Zou G.H., Pan D.Y. and Zhao H.Y. (2003). Genotyping error detection through tightly linked markers. Genetics 164 (3): 1161-1173.
  • Zou G.H. and Zhao H.Y. (2003). Haplotype frequency estimation in the presence of genotyping errors. Human Heredity 56 (1-3): 131-138.
  • Wan A.T.K. and Zou G.H. (2003). Optimal critical values of pre-tests when estimating the regression variance: analytical findings under a general loss structure. Journal of Econometrics 114 (1): 165-196.
  • Zou G.H. and Wan A.T.K. (2003). Admissible and minimax estimation of the parameter n in the binomial distribution. J.Statist.Plann.Inference 113 (2): 451-466.
  • Zou G.H. and Li Y. (2003). On variance estimation of the ratio estimator under two-phase sampling. Statistics 37 (1): 1-16.
  • Zou G.H. and Wan A.T.K. (2000). Simultaneous estimation of several stratum means under error-in-variables superpopulation models. Ann. Inst. Statist. Math. 52 (2): 380-396.

 

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