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Qi Zheng, PhD

Dr. Qi Zheng Image

Bioinformatics and Biostatistics   
Associate Professor 
Room No.123, 485 E. Gray St
Louisville, KY 40202
Phone: 502-852-8780
Fax: 502-852-3294
qi.zheng@louisville.edu
Personal Website

CURRICULUM VITAE

 

Background


Postdoctoral fellow (2015) Bioinformatics and Biostatistics, Emory University.

Ph.D. (2013) Mathematical Science, Clemson University.

B.S. (2006) Mathematics and Applied Mathematics, Nanjing University, China

I joined the Department of Bioinformatics and Biostatistics in Fall of 2015.

Research Interests

My primary research interests are high dimensional data analysis, semiparametric and nonparametric models, varying effects survival analysis, personalized treatments, tree-based learning methods, and statistical applications in epidemiology and biomedical sciences.

Selected Publications

Fei, Z., Zheng, Q., Hong, H., and Li, Y. (2021+) Inference for high dimensional censored quantile regression.  Accepted by Journal of American Statistical Association.

Cui, Y., Wu, R., and Zheng, Q.(2020) Estimation of change-point for a class of count time series models.  Accepted by Scandinavian Journal of Statistics

Pijyan, A., Zheng, Q., Hong, H., and Li, Y. (2020) Consistent estimation of generalized linear models with high dimensional predictors via stepwise regression. Entropy 22 (9), 965.

Zheng, Q., Hong, H., and Li, Y. (2020) Building generalized linear models with ultrahigh dimensional features:  a sequentially conditional approach. Biometrics 76 (1), 47–60.

Lin, J., Wang, D., and Zheng, Q. (2019) Regression analysis and variable selection for two-stage multiple-infection group testing data. Statistics in Medicine 38 (23), 4519–4533.

Hong, H., Zheng, Q., and Li, Y. (2019) Forward regression for Cox models with high-dimensional covariates. Journal of Multivariate Analysis 173, 268–290.

Zheng, Q., Peng, L., and He, X. (2018) High dimensional censored quantile regression. Annals of Statistics 46 (1), 308–343

Cui, Y. and Zheng, Q. (2017) Conditional maximum likelihood estimation for a class of observation- driven time series models for count data. Statistics & Probability Letters 123, 193–201.

Zheng, Q., Gallagher, C., and Kulasekera, K.B. (2017) Robust adaptive Lasso for variable selection.  Communication in Statistics - Theory and Methods 46 (9), 4642--4659.

Zheng, Q. and Peng, L. (2017) Consistent model identification of varying coefficient quantile regression with BIC tuning parameter selector. Communication in Statistics - Theory and Methods 46 (3), 1031--1049.

Li, J., Zheng, Q., Peng, L., and Huang, Z. (2016) Survival impact index and ultrahigh-dimensional model-free screening with survival outcomes.  Biometrics 72 (4), 1145--1154.

Zheng, Q., Peng, L., and He, X. (2015) Globally adaptive quantile regression with ultrahigh dimensional data. Annals of Statistics, 43 (5), 2225--2258.

Zheng, Q., Gallagher, C., and Kulasekera, K.B. (2013) Adaptively weighted kernel regression, Journal of Nonparametric Statistics, 25 (4), 855-872.

Zheng, Q., Kulasekera, K.B., and Gallagher, C. (2013) Adaptive penalized quantile regression for high dimensional data, Journal of Statistical Planning and Inference, 142 (6), 1029-1038.

Zheng, Q., Gallagher, C., and Kulasekera, K.B. (2013) The growth rate of significant regressors for high dimensional data, Statistics & Probability Letters, 83 (9), 1969-1972.

Zheng, Q., Kulasekera, K.B., and Gallagher, C. (2010) Local adaptive smoothing in kernel regres- sion estimation, Statistics & Probability Letters, 80 (7-8), 540-547.

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