Publications

[1] Liu, C.* (2006) On a large sample problem in nonlinear regression. Acta Mathematica Scientia. 26B(3), 385-394.

[2] Wang, Q., Liu, W., and Liu, C. (2009) Probability density estimation for survival data with censoring indicators missing at random. Journal of Multivariate Analysis. 100(5), 835-850.

[3] Liu, C.* and Wang, Q. (2010) Semiparametric estimation for regression coefficients in the Cox model with failure indicators missing at random. Statistica Sinica. 20, 1125-1142.

[4] Liu, A., Liu, C., Li, Q., Yu, K.F., and Yuan V.W. (2010) A threshold sample-enrichment approach in a clinical trial with heterogeneous subpopulations. Clinical Trials. 7(5), 537-545.

[5] Liu, A., Li, Q., Liu, C., Yu, K., and Yu, K.F. (2010) A Rank-based test for Comparison of multidimensional outcomes. Journal of the American Statistical Association. 105(490), 578-587.

[6] Liu, C., Liu, A., and Halabi, S. (2011) A min-max combination of biomarkers to improve diagnostic accuracy. Statistics in Medicine. 30(16), 2005-2014.

[7] Tian, G., Tang, M., and Liu, C. (2012) Accelerating the quadratic lower- bound algorithm via optimizing shrinkage parameter. Computational Statistics & Data Analysis. 56, 255-265.

[8] Wang, Q., Dinse, G.E., and Liu, C. (2012) Hazard function estimation with cause-of-death data missing at random. Annals of the Institute of Statistical Mathematics. 64(2), 415-438.         

[9] Tang, L., Liu, A., Schisterman, E.F., Zhou, X., and Liu, C. (2012) Homogeneity tests of clustered diagnostic markers with applications to the BioCycle Study. Statistics in Medicine. 31(28), 3638-3648.

[10] Wei, C. and Liu, C. (2012) Statistical inference on semiparametric partially linear additive models. Journal of Nonparametric Statistics. 24(4), 809-823.

[11] Liu, A., Liu, C.*, Zhang, Z., and Albert, P.S. (2012) Optimality of group testing in the presence of misclassification. Biometrika. 99(1), 245-251.

[12] Tang, L., Kang, L., Liu, C., Schisterman, E.F. and Liu, A. (2013) An additive selection of markers to improve diagnostic accuracy based on a discriminatory measure. Academic Radiology. 20(7), 854-862.

[13] Liu, C., Liu, A., Zhang, B., and Zhang, Z. (2013) Improved confidence intervals of a small probability from pooled testing with misclassification. Frontiers in Epidemiology.

[14] Dong, T.§, Liu, C., Petricoin, E.F., and Tang, L. (2014) Combining markers with and without the limit of detection. Statistics in Medicine. 33(8), 1307-1320.

[15] Liu, C., Liu, A., Hu, J., Yuen, V., and Halabi, S. (2014) Adjusting for misclassification in a stratified biomarker clinical trial. Statistics in Medicine. 33(18), 3100-3113.

[16] Tian, R.§, Xue, L., and Liu, C. (2014) Penalized quadratic inference functions for semiparametric varying coefficient partially linear models with longitudinal data. Journal of Multivariate Analysis. 132, 94-110.

[17] Zhang, Z., Liu, C., Kim, S., and Liu, A. (2014) Prevalence estimation subject to misclassification: the mis-substitution bias and some remedies. Statistics in Medicine. 33, 4482-4500.

[18] Wei, C., Wan, L., and Liu, C.* (2015) Efficient estimation in heteroscedastic partially linear varying coefficient models. Communications in Statistics: Simulation and Computation. 44(4), 892-901.

[19] Liu, C, Guo, S., and Wei, C., (2016) Principle components regression estimator of  the parameters in partially linear models. Journal of Statistical Computation and Simulation. 86(15), 3127-3133. 

[20] Shen, J., Yuen, K.C., and Liu, C.* (2016) Empirical likelihood confidence regions for one- or two- samples with doubly censored data. Computational Statistics & Data Analysis.  93, 285-293.

[21] Shen, J., Yu, H. §, Yang, J. §, and Liu, C.* (2019) Semiparametric Bayesian analysis for longitudinal mixed effects models with non-normal AR(1) errors. Statistics and Computing.  29 (3), 571-583.

[22] Liu, Z.§, Liu, C., and Sun, Z. (2019) Consistent model check of errors-in-variables varying coefficient model with auxiliary variable. Journal of Statistical Planning and Inference. 198, 13-28.

[23] Zhang, Z., Tang, L.§, Liu, C., and Berger, V. W. (2019) Conditional estimation and inference to address observed covariate imbalance in randomized clinical trials. Clinical Trial. 16 (2), 122-131.

[24] Zhang, Z. Hu, Z. and Liu, C. (2019) Estimating the population average treatment effect in observational studies with choice-based sampling. International Journal of Biostatistics. 15(1), doi.org /10.1515/ijb-2018-0093.

[25] Zhang, Z., Liu, C., Ma, S. and Zhang, M. (2019) Estimating Mann-Whitney-Type causal effects for right-censored survival outcomes. Journal of Causal Inference. 7(1), doi.org/10.1515/ jci-2018-0010.

[26] Zhang, Z. Ma, S., Shen, C. and Liu, C. (2019) Estimating Mann–Whitney‐type Causal Effects. International Statistical Review. 87(3), 514-530.

[27] Wang, M.§, Liu, C., Xie, T., and Sun, Z. (2020) Data-driven model checking for errors-in-variables varying-coefficient models with replicate measurements. Computational Statistics & Data Analysis. 141, 12-27.

[28] Lu, S.§, Chen, X., Xu, S.§, and Liu, C. (2020). Joint model-free feature screening for ultra-high dimensional semicompeting risks data. Computational Statistics & Data Analysis. 147, 106942. doi.org/10.1016 /j.csda.2020.106942.

[29] Wang, X§. Yang, L§. Zhang, H., Yang*, Z. and Liu, C.* (2021) Forecasting confirmed cases of the COVID-19 pandemic with a migration-based epidemiological model. Statistics and its interface, 14(1), 59-71.

[30] Chen, X., Liu, C., and Xu, S.*§ (2021) An efficient algorithm for joint feature screening in ultrahigh-dimensional Cox’s model. Computational Statistics. 36, 885-910.

[31] Tang, N., Liu, C., Shi, J., and Huang, Y. (2022) Editorial: Bayesian Inference and AI. Front. Big Data 5, 934362. doi:10.3389/fdata.2022.934362.

[32] Zhu, W.§, Xu, S.§,  Liu, C., and Li, Y. (2023) Minimax powerful functional analysis of covariance tests with application to longitudinal genome-wide association studies. Scandinavian Journal of Statistics. 50(1), 266-295.

[33] Liu, X., Fan, D.§, Zhang, X.*, and Liu, C. (2023) Empirical likelihood-based portmanteau tests for autoregressive moving average models with possible infinite variance innovations. Statistics and its interface, 16(2), 337-347.

[34] Zhang, Y.§, Zhang, X., Zhang, H., Liu, A., and Liu, C.* (2023) Low-rank latent matrix-factor prediction modeling for generalized high-dimensional matrix-variate regression. Statistics in Medicine. doi.org/10.1002/sim.9821.

[35] Zhong, C.§, Li, Y.§, Yang, D., Li, M., Zhou, X., Fu, B.*,  Liu, C. C.* & Welsh, A. H. (2023). CeCNN: Copula-enhanced convolutional neural networks in joint prediction of refraction error and axial length based on ultra-widefield fundus images. arXiv preprint arXiv:2311.03967. (https://arxiv.org/abs/2311.03967)

[36] Zhang, X.,  Liu, C. C.*, Guo, J.*, Yuen, K. C., & Welsh, A. H. (2024). Modeling and Learning on High-Dimensional Matrix-Variate Sequences. Journal of the American Statistical Association, 1-16.

[37] Li, Y.§, Huang, Q.§, Zhong, C.§, Yang, D., Li, M., Welsh, A. H., Liu, A.,  Fu, B.*,  Liu, C. C.*& Zhou, X.* (2024). OUCopula: Bi-Channel Multi-Label Copula-Enhanced Adapter-Based CNN for Myopia Screening Based on OU-UWF Images. In International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization. (https://doi.org/10.48550/arXiv.2403.11974)

[38] Song, Y., Yeung, E., Liu, A., VanderWeele, T.J., Chen, L., Lu, C., Liu, C., Schisteman, E.F., Ning, Y., and Zhang, C. (2012) Pancreatic beta-cell function and type 2 diabetes risk: quantify the causal effect using a Mendelian randomization approach based on meta-analyses. Human Molecular Genetics. 21(22), 5010-5018. 

My role: This was a joint work between Harvard University and National Institutes of Health (NIH), USA. Dr. Aiyi Liu and I took charge of all the statistical analysis, including data clearance and method design.

[39] Yau, G., Lee J., Tam, V., Liu, C., Chu, B., and Yuen, C. (2014) Differences in risk factors for retinopathy of prematurity development in paired twins: a Chinese population study. The Scientific World Journal. DOI: 10.1155/2014/212183.

[40] Lee, J., Liu, C., Chan, J., and Lai, J. (2014) Predictors of success in selective laser trabeculoplasty for Chinese open angle glaucoma, Journal of Glaucoma. 23(5), 321-325.

[41] Lee, J., Liu, C., Chan, J., and Lai, J. (2014) Predictors of success in selective laser trabeculoplasty for normal tension glaucoma. Medicine (Baltimore). 93(28), e236.

[42] Lee, J., Chan, J., Chang, R., Singh, K., Liu, C., Gangwani, R., Wong, M., and Lai, J. (2014) Corneal changes after a single session of selective laser trabeculoplasty for open angle glaucoma. Eye(Lond). 28(1), 47-52. 

[43] Yau, G., Lee, J., Tam, V., Liu, C., and Wong, I. (2014) Risk factors for retinopathy of prematurity in extreme preterm Chinese infants. Medicine (Baltimore). 93(28), e314.

[44] Lee, J., Liu, C., Chan, J., Wong, R., Wong, I., and Lai, J. (2014) Predictors of success in selective laser trabeculoplasty for primary open angle glaucoma in Chinese. Clinical Ophthalmology. 8, 1787-1791.

[45] Yau, G., Lee, J., Tam, V., Yip, S., Cheng, E., Liu, C., Chu, B., and Yuen, C. (2015) Incidence and risk factors for retinopathy of prematurity in multiple gestations: a Chinese population study. Medicine (Baltimore). 94(18), e867. 

[46] Lee, J., Wong, M., Liu, C., and Lai, J. (2015) Optimal selective laser trabeculoplasty energy for maximal intraocular pressure reduction in open angle glaucoma. Journal of Glaucoma. 24(5), e128-e131. 

[47] Yau, G., Lee, J., Tam, V., Liu, C., Chu, B., and Yuen, C. (2015) Incidence and risk factors for retinopathy of prematurity in extreme low birth weight Chinese infants. International Ophthalmology. 35(3), 365-373. 

[48] Yau, G. Lee, J., Tam, V., Liu, C., Yip, S., Cheng, E., Chu, B., and Yuen, C. (2016) Incidence and risk factors of retinopathy of prematurity from 2 neonatal intensive care units in a Hong Kong Chinese population. Asia Pacific Journal of Ophthalmology.  5(3), 185-191.

[49] Xing, W., Wang, W., Shao, Q., Yong, B. Liu, C., Feng, X., and Dong, Q., (2019) Estimating monthly evapotranspiration by assimilating remotely sensed water storage data into the extended Budyko framework across different climatic regions. Journal of Hydrology. 567, 684-695. 

[50] Wei, J., Wang, W., Shao, Q., Rong, Y., Xing, W., and Liu, C. (2020) Influence of mature El Niño‐Southern Oscillation phase on seasonal precipitation and streamflow in the Yangtze River Basin, China. International Journal of Climatology 40(8), 3885-3905

[51] Choy, B., Ng, L., Zhu, M., Liu, C., Xu, S.§, & Lai, J. (2020). Randomized control trial on the effectiveness of collagen crosslinking on bullous keratopathy. Cornea. 39(11), 1341-1347

[49] Zhong, C.§, Ma, Z., Shen, J., and Liu, C.* (2021)  Dependent Dirichlet Processes for Analysis of a Generalized Shared Frailty Model. López-Ruiz, R. Ed. Computational Statistics and Applications. IntechOpen. doi.org/10.5772/intechopen.101502

[50] Li, Y., Xu, S.§, and Liu, C. (2021) . Functional Data Modeling and Hypothesis Testing for Longitudinal Alzheimer Genome-Wide Association Studies. In: Zhao, Y., Chen, (.DG. (eds) Modern Statistical Methods for Health Research. Emerging Topics in Statistics and Biostatistics. 351-379， Springer, Cham. https://doi.org/10.1007/978-3-030-72437-5_16

[51] Shen, J., and Liu, C.* (2020) Bayesian analysis for random effects models. Bayesian inference on complicated data. Tang, L. Ed. IntechOpen. DOI: 10.5772/intechopen.88822

[52] Liu, A., Liu, C., and Yu, K. F. (2010) Group sequential methods in biomedical research. Encyclopedia of Statistical Science. Wiley Online Library: http://onlinelibrary.wiley. com/doi/10.1002/0471667196. ess7128/pdf or Wiley StatsRef: Statistics Reference Online: DOI: 10.1002/9781118445112.stat00185.

[53] Liu, A., Liu, C., and Yu, K. F. (2009) Group sequential methods in biomedical research. Methods and Applications of Statistics in the Life and Health Sciences. Balakrishnan, N. Ed. 365-376. Wiley.

[54] 吴密霞 & 劉春玲 （2014）多元統計分析: 現代數學基礎叢書， 科學出版社

（In English: Wu, M. & Liu, C. (2014) Multivariate Statistical Analysis, Science Press, China）

[1]  Zhong, C.§*, Yang, J., Shen, J., Liu, C., and Li, Z. (2022+) On MCMC mixing under unidentified nonparametric models: Application to survival predictions under  transformation models. 

[2] Zhong, C.§, Ma, Z., Zhang, X., and Liu, C.* (2023+) Non-segmental Bayesian detection of  multiple change-points. Under review at JRSSb.

[3] Ma, Z., Zhong, C.§, Shen, J., and Liu, C.* (2023+) Detection of imperceptible change-points by jump sizes with an application to London house index. Under review at Bayesian Analysis.

[4] Zhang, X., Li, G., Liu, C*., and Guo, J.* (2023+) Tucker tensor factor models: matricization and mode-wise PCA estimation. Under review at Science China.

[5] Li, Y.§, Deng, J.§, Zhong, C.§, Yang, D., Li, M., Welsh, A. H., Liu, A.,  Fu, B.*,  Liu, C. C.*& Zhou, X.* (2024+) OU-CoViT: Copula-Enhanced Bi-Channel Multi-Task Vision Transformers with Dual Adaptation for OU-UWF Images.Under review at NIPS.

[1] Li, W.§, Zhong, C.§, Fu, B.*, and Liu, C.* (2023+) Bayesian copula for mat2vec regression with    correlated responses.

[2] Zhong, C.§, Ma, H., Xu, S., Liu, C.*, and Li, Y.* (2023+) Quantile-based functional classification for uni- and high-dimensional functional data.

[3] Lee, J., Zhang, X., and Liu, C.* (2023+) Detection of structural breaks in high-dimensional matrix-variate factor models.

[4] Zhang, X.*, Fang, X.§, Xu, S., Zhu, X., and Liu, C.* (2023+) Joint feature screening incorporating network structure among responses.