# Faculty Research Articles

## 2023 Faculty Publications

Arsham, A., Bebu, I., and Mathew, T. (2023). Cost-effectiveness analysis under multiple effectiveness outcomes: a probabilistic approach. Stat. Med., 42(22):3936–3955.

Ay, A., Landon, J., Ruggeri, F., and Soyer, R. (2023). A latent-factor self-exciting point process for software failures. Naval Res. Logist., 70(6):584–600.

Bhutani, K. R., Kalpathy, R., and Mahmoud, H. (2023). Degrees in random m-ary hooking networks. Compositionality, 5(6):21.

Bhutani, K. R., Kalpathy, R., Mahmoud, H., and Ofonedu, A. (2023). Some empirical and theoretical attributes of random multi-hooking networks. Int. J. Comput. Math. Comput. Syst. Theory, 8(4):222–234.

Ceniceros, J., Christiana, A., and Nelson, S. (2023). Psyquandle coloring quivers. J. Knot Theory Ramifications, 32(11):Paper No. 2350073, 18.

Du, H., Li, Z., Liu, J., Liu, Y., and Sun, J. (2023). Divide-and-conquer DNN approach for the inverse point source problem using a few single frequency measurements. Inverse Problems, 39(11):Paper No. 115006, 19.

Li, X., Liang, H., Hardle, W., and Liang, H. (2023). Use generalized linear models or generalized ¨ partially linear models? Statistics and Computing, 33(5):Paper No. 101, 10.

Liu, Y. and Hu, F. (2023). The impacts of unobserved covariates on covariate-adaptive randomized experiments. Ann. Statist., 51(5):1895–1920.

Miao, R., Zhang, X., and Wong, R. K. W. (2023). A wavelet-based independence test for functional data with an application to MEG functional connectivity. J. Amer. Statist. Assoc., 118(543):1876–1889.

Modarres, R. (2023). Nonparametric classification of high dimensional observations. Statist. Papers, 64(6):1833–1859. 1

Peng, X. and Judy Wang, H. (2023). Inference for joint quantile and expected shortfall regression. Stat, 12:Paper No. e619, 14.

Qi, Z., Pang, J.-S., and Liu, Y. (2023). On robustness of individualized decision rules. J. Amer. Statist. Assoc., 118(543):2143–2157.

Qin, Y., Li, Y., Ma, W., Yang, H., and Hu, F. (2024). Adaptive randomization via Mahalanobis distance. Statist. Sinica, 34(1):353–375.

Riedel, M. and Mahmoud, H. (2023). Egorychev method: a hidden treasure. Matematica, 2(4):893–933.

Wang, J., Qi, Z., and Wong, R. K. W. (2023). Projected state-action balancing weights for offline reinforcement learning. Ann. Statist., 51(4):1639–1665.

Wang, Y., Wang, H. J., and Tang, Y. (2023). Score-based test in high-dimensional quantile regression for longitudinal data with application to a glomerular filtration rate data. Stat, 12:Paper No. e610, 10.

Wu, M., Sun, X., Liu, A., Peng, C., and Li, Z. (2023). Significance tests for covariates in the diagnostic accuracy index of a biomarker against a continuous gold standard. Stat. Med., 42(22):4015–4027.

Xue, W., Zhang, X., Chan, K. C. G., and Wong, R. K. W. (2024). RKHS-based covariate balancing for survival causal effect estimation. Lifetime Data Anal., 30(1):34–58.

Yang, J., Chen, G., Wei, C., Sergeev, A., Huang, J. K., Scully, M. M., Krantz, S. G., Yao, P., Guo, T., Wang, J., Yang, Z., and Chen, M.-C. (2023). Animal shapes, modal analysis, and visualization of motion (IV): geometric constructions and implementation. J. Geom. Anal., 33(10):Paper No. 330, 39.

Yuan, M. and Diao, G. (2024). Sieve maximum likelihood estimation for generalized linear mixed models with an unknown link function. Stat. Interface, 17(1):39–49. 2

Zhang, D., Wang, Y., and Liang, H. (2023). A novel estimation method in generalized single index models. Journal of Business & Economic Statistics, 41(2):399–413.

Zhu, R., Liang, H., and Ruppert, D. (2023). Ensemble subset regression (ensure): efficient high-dimensional prediction. Statistica Sinica, 33:1411–1434.

Zhu, R., Wang, H., Zhang, X., and Liang, H. (2023b). A scalable frequentist model averaging method. Journal of Business & Economic Statistics, 41(4):1228–1237.

## 2022 Faculty Publications

Abdin, T., Mahmoud, H., Modarres, A., and Wang, K. (2022). An index for betting with examples from games and sports. The Mathematical Gazette, 106(565):32–40.

Aguech, R., Bose, S., Mahmoud, H., and Zhang, Y. (2022). Some properties of exponential trees. International Journal of Computer Mathematics. Computer Systems Theory, 7(1):16–32.

Gastwirth, J. L. (2022). A summary of the statistical aspects of the procedures for resolving potential employment discrimination recently issued by the office of federal contract compliance along with a commentary. Law, Probability and Risk.

Gastwirth, J. L. and Shi, Q. (2022). Comparing the secular increasing trend and effect of the response to the 2008 financial recession on wealth inequality in the u.s. with other nations using the median-based gini index. Journal of Quantitative Economics.

Gastwirth, J. L., Miao, W., and Pan, Q. (2022). On the interplay between practical and statistical significance in equal employment cases. Law, Probability and Risk.

Guo, M., Nguyen, L., Du, H., and Jin, F. (2022). When patients recover from covid-19: Data- driven insights from wearable technologies. Frontiers in Big Data, page 34.

Hossen, I., Anders, M. A., Wang, L., and Adam, G. C. (2022). Data-driven rram device models using kriging interpolation. Scientific Reports, 12(1):1–12.

Landon, J. and Gastwirth, J. (2022). Graphical measures summarizing the inequality of income of two groups. Statistics and Public Policy, 9(1):20–25.

Lyon, M. and Mahmoud, H. (2022). Insertion depth in power-weight trees. Information Pro- cessing Letters, 176:Paper No. 106227, 9.

Mahmoud, H. (2022). Profile of random exponential recursive trees. Methodology and Comput- ing in Applied Probability, 24(1):259–275.

Miao, R., Xue, W., and Zhang, X. (2022a). Average treatment effect estimation in observational studies with functional covariates. Statistics and Its Interface, 15(2):237–246.

Miao, W., Pan, Q., and Gastwirth, J. L. (2022b). A misuse of statistical reasoning: The statistical arguments offered by texas to the supreme court in an attempt to overturn the results of the 2020 election. Statistics and Public Policy, 9(1):67–73.

Modarres, R. (2022). Nonparametric tests for detection of high dimensional outliers. Journal of Nonparametric Statistics, 34(1):206–227.

Song, D., Xi, N. M., Li, J. J., and Wang, L. (2022). scsampler: fast diversity-preserving sub- sampling of large-scale single-cell transcriptomic data. Bioinformatics.

Sparks, J., Balaji, S., and Mahmoud, H. (2022). The containment profile of hyper-recursive trees. Journal of Applied Probability, 59(1):278–296.

Su, J., Dougherty, E. T., Jiang, S., and Jin, F. (2022). An interactive knowledge graph based plat- form for covid-19 clinical research. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pages 1609–1612.

Tang, Y., Wang, Y., Wang, H. J., and Pan, Q. (2022). Conditional marginal test for high dimen- sional quantile regression. Statistica Sinica, 32(2):869–892.

Wang, J., Li, X., and Liang, H. (2022a). A new exact p-value approach for testing variance homogeneity. Statistical Theory and Related Fields, 6(1):81–86.

Wang, L. and Xu, H. (2022). A class of multilevel nonregular designs for studying quantitative factors. Statistica Sinica, 32(2):825–845.

Wang, Q., Xue, W., Zhang, X., Jin, F., and Hahn, J. (2022b). S2flnet: Hepatic steatosis detection network with body shape. Computers in biology and medicine, 140:105088.

Wang, Y., Zhao, Y., and Pan, Q. (2022c). Advances, challenges and opportunities of phyloge- netic and social network analysis using covid-19 data. Briefings in Bioinformatics, 23(1):bbab406.

Xu, W., Lin, H., Zhang, R., and Liang, H. (2022a). Two-sample functional linear models with functional responses. Journal of Statistical Planning and Inference, 218:85–105.

Xu, W., Wang, H. J., and Li, D. (2022b). Extreme quantile estimation based on the tail single- index model. Statistica Sinica, 32(2):893–914.

Yang, Z., Bradshaw, S., Hewett, R., and Jin, F. (2022). Discovering opioid use patterns from social media for relapse prevention. Computer, 55(2):23–33.

## 2021 Faculty Publications

Abu-gellban, H., Nguyen, L., and Jin, F. (2021). Gfdlecg: Pac classification for ecg signals using gradient features and deep learning. In Advances in Data Science and Information Engineering, pages 369–382. Springer.

Ay, A., Soyer, R., Landon, J., and Özekici, S. (2021). Bayesian analysis of doubly stochastic Markov processes in reliability. Probability in the Engineering and Informational Sciences, 35(3):708–729.

Bhutani, K., Kalpathy, R., and Mahmoud, H. (2021). Average measures in polymer graphs. International Journal of Computer Mathematics. Computer Systems Theory, 6(1):37–53.

Chai. J. and Nayak, T. K. (2021). Minimax randomized response methods for protecting respondent’s privacy. Communications in Statistics - Theory and Methods.

Chen, Y. and Zhang, X. (2021). A P2-P1 partially penalized immersed finite element method for Stokes interface problems. International Journal of Numerical Analysis and Modeling, 18(1):120– 141.

Du, H., Barut, E., and Jin, F. (2021). Uncertainty quantification in cnn through the bootstrap of convex neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 12078–12085.

Feng, Y. and Mahmoud, H. M. (2021). Dynamic Po´lya-Eggenberger urns. Statistics & Proba- bility Letters, 174:Paper No. 109089, 7.

Gastwirth, J. L. (2021). How the legal systems failure to appreciate statistical evidence disad- vantages plaintiffs in discrimination cases. pages 255–272. Springer.

Jiang, Y., Pan, Q., Liu, Y., and Evans, S. (2021). A statistical review: why average weighted accuracy, not accuracy or auc? Biostatistics & Epidemiology, 5(2):267–286.

Lee, J., Sun, Y., and Wang, H. J. (2021). Spatial cluster detection with threshold quantile regres- sion. Environmetrics, 32(8):Paper No. e2696, 15.

Li, X., Wang, L., and Wang, H. J. (2021). Sparse learning and structure identification for ultrahigh-dimensional image-on-scalar regression. Journal of the American Statistical Associa- tion, 116(536):1994–2008.

Luo, S., Barut, E., and Jin, F. (2021). Statistically consistent saliency estimation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 745–753.

Ma, W., Zhang, L.-X., and Hu, F. (2021). Comment on ‘Inference after covariate-adaptive randomisation: aspects of methodology and theory’ [ 4311482]. Statistical Theory and Related Fields, 5(3):187–189.

Modarres, R. (2021). On the blocks of interpoint distances. Journal of the Iranian Statistical Society. (JIRSS), 20(1):197–218.

Nayak, T. K. (2021). A review of rigorous randomized response methods for protecting respondent's privacy and data confidentiality. In Methodology and Applications of Statistics: A Volume in Honor of C.R. Rao on the Occasion of his 100th Birthday (eds. B.C. Arnold, N. Balakrishnan and C.A. Coelho), New York: Springer, pp. 319–341.

Oviedo, R. J., Nayak, T. K., Liu, Y., Zhang, S. and Zhao, F. (2021). Laparoscopic duodenal switch versus Roux-en-Y gastric bypass at a high-volume community hospital: A retrospective cohort study from a rural setting. Obesity Surgery.

Oviedo, R. J., Nayak, T., Long, Z. and Yan, M. Robotic. (2021). Roux en Y gastric bypass can be safe and cost-effective in a rural setting: Clinical outcomes from a community hospital bariatric program. Journal of Robotic Surgery, 15:929–936.

Saunders, A. and Mahmoud, H. (2021). Bar bets and generating functions: the distribution of the separation of two distinct card ranks. American Mathematical Monthly, 128(3):258–264.

Sun, Z., Chen, F., Liang, H., and Ruppert, D. (2021). A projection-based consistent test incor- porating dimension-reduction in partially linear models. Statistica Sinica, 31(3):1489–1508.

Wang, H., Zhang, D., Liang, H., and Ruppert, D. (2021a). Iterative likelihood: a unified infer- ence tool. Journal of Computational and Graphical Statistics, 30(4):920–933.

Wang, L., Elmstedt, J., Wong, W. K., and Xu, H. (2021b). Orthogonal subsampling for big data linear regression. The Annals of Applied Statistics, 15(3):1273–1290.

Wang, Q., Lu, Y., Zhang, X., and Hahn, J. (2021c). Region of interest selection for functional features. Neurocomputing, 422:235–244.

Wang, Q., Xue, W., Zhang, X., Jin, F., and Hahn, J. (2021d). Pixel-wise body composition prediction with a multi-task conditional generative adversarial network. Journal of Biomedical Informatics, 120:103866.

Wu, S. Y., Li, X., Xia, Y., and Liang, H. (2021). A novel model checking approach for dose- response relationships. Statistical Methods in Medical Research, 30:2119–2129.

Xu, W. and Wang, H. J. (2021). Discussion on “On studying extreme values and systematic risks with nonlinear time series models and tail dependence measures” [ 4232528]. Statistical Theory and Related Fields, 5(1):26–30.

Yang, L., Pan, Q., and Zhao, Y. (2021). Integrative clustering analysis with application in multi-source gene expression data. Journal of Data Science, pages 1–20.

Zhang, C. and Nayak, T. K. (2021). Post-randomization for controlling identification risk in releasing microdata from general surveys. Journal of Applied Statistics, 48(3):455–470.

Zhai, X. and Nayak, T. K. (2021). A post-randomization method for rigorous identification risk control in releasing microdata. Journal of Statistical Theory and Practice, 15(8).

Zhang, Q., Chen, F., Wu, S., and Liang, H. (2021). A simple yet powerful test for assessing goodness-of-fit of high-dimensional linear models. Statistics in Medicine, 40:3153–3166.

Zhang, X., Xue, W., and Wang, Q. (2021). Covariate balancing functional propensity score for functional treatments in cross-sectional observational studies. Computational Statistics & Data Analysis, 163:Paper No. 107303, 15.

Zhao, S., Zhang, X., Jin, F., and Hahn, J. (2021). An auxiliary tasks based framework for auto- mated medical skill assessment with limited data. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pages 1613–1617. IEEE.

## 2020 Faculty Publications

Chen, Y., Hou, S., and Zhang, X. (2020). A bilinear partially penalized immersed finite element method for elliptic interface problems with multi-domain and triple-junction points. Results in Applied Mathematics, 8:Paper No. 100100, 13.

Domicolo, C. and Mahmoud, H. (2020). Degree-based Gini index fro graphs. Probability in the Engineering and Informational Sciences, 34(2):157–171.

Gao, Z., Tang, Y., Wang, H. J., Wu, G. K., and Lin, J. (2020). Automatic identification of curve shapes with applications to ultrasonic vocalization. Computational Statistics & Data Analysis, 148:106956, 18.

Gastwirth, J. L. (2020). The role of statistical evidence in civil cases. Annual Review of Statistics and its Application, 7:39–60.

Guo, L. and Modarres, R. (2020a). Nonparametric change point detection for periodic time series. The Canadian Journal of Statistics, 48(3):518–534.

Guo, L. and Modarres, R. (2020b). Nonparametric tests of independence based on interpoint distances. Journal of Nonparametric Statistics, 32(1):225–245.

Guo, L. and Modarres, R. (2020c). Testing the equality of matrix distributions. Statistical Methods & Applications, 29(2):289–307.

He, F., Wang, H. J., and Tong, T. (2020). Extremal linear quantile regression with Weibull-type tails. Statistica Sinica, 30(3):1357–1377.

Huang, H., Li, Y., Liang, H., and Tang, Y. (2020a). Estimation of single-index models with fixed censored responses. Statistica Sinica, 30(2):829–843.

Huang, H., Shangguan, J., Li, Y., and Liang, H. (2020b). Bi-level variable selection in high dimensional Tobit models. Statistics and its Interface, 13(2):151–156.

Hudson, G. M., Lu, Y., Zhang, X., Hahn, J., Zabal, J. E., Latif, F., and Philbeck, J. (2020). The development of a BMI-guided shape morphing technique and the effects of an individualized figure rating scale on self-perception of body size. European Journal of Investigation in Health, Psychology and Education, 10(2):579–594.

Jana, K., Sengupta, D., Kundu, S., Chakraborty, A., and Shaw, P. (2020). The statistical face of a region under monsoon rainfall in eastern India. Journal of the American Statistical Association, 115(532):1559–1573.

Jayachandra, V., Kesidi, R., Yang, Z., Zhang, C., Pan, Z., Sheng, V., and Jin, F. (2020). Besober: Assisting relapse prevention in alcohol addiction using a novel mobile app-based intervention. In 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 944–947. IEEE.

Liang, S., Yang, Z., Jin, F., and Chen, Y. (2020). Data centers job scheduling with deep rein- forcement learning. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 906–917. Springer.

Liu, Y. and Gastwirth, J. L. (2020). On the capacity of the Gini index to represent income distributions. Metron, 78(1):61–69.

Lyon, M. R. and Mahmoud, H. M. (2020). Trees grown under young-age preferential attach- ment. Journal of Applied Probability, 57(3):911–927.

Ma, W., Qin, Y., Li, Y., and Hu, F. (2020). Statistical inference for covariate-adaptive random- ization procedures. Journal of the American Statistical Association, 115(531):1488–1497.

Mahmoud, H. (2020). A model for the spreading of fake news. Journal of Applied Probability, 57(1):332–342.

Mahmoud, H. M. and Zhang, P. (2020). Distributions in the constant-differentials Po´lya process. Statistics & Probability Letters, 156:108592, 6.

Modarres, R. (2020). Graphical comparison of high-dimensional distributions. International Statistical Review. Revue Internationale de Statistique, 88(3):698–714.

Modarres, R. and Song, Y. (2020a). Interpoint distances: applications, properties, and visual- ization. Applied Stochastic Models in Business and Industry, 36(6):1147–1168.

Modarres, R. and Song, Y. (2020b). Multivariate power series interpoint distances. Statistical Methods & Applications. Journal of the Italian Statistical Society, 29(4):955–982.

Pan, Q., Miao, W., and Gastwirth, J. L. (2020a). Statistical procedures for assessing the need for an affirmative action plan: A reanalysis of shea v. kerry. Statistics and Public Policy, 7(1):1–8.

Pan, Z., Mehta, D., Tiwari, A., Ireddy, S., Yang, Z., and Jin, F. (2020b). An interactive platform to track global covid-19 epidemic. In 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 948–951. IEEE.

Raman, B., Ravishanker, N., Soyer, R., Gorti, V., and Sen, K. (2020). Dynamic Bayesian mod- eling of multiple count time series using r-inla. Journal of the Indian Statistical Association, 58(2):157–192.

Ruan, P., Wang, S., and Liang, H. (2020). mirPLS: a partial linear structure identifier method for cancer subtyping using MicroRNAs. Bioinformatics, 36:4902–4909.

Sun, J., Du, P., Miao, H., and Liang, H. (2020). Robust feature screening procedures for single and mixed types of data. Journal of Statistical Computation and Simulation, 90(7):1173–1193.

Tang, Y. and Pan, Q. (2020). Conditional marginal test for high dimensional quantile regression. Statistica Sinica.

Xiao, X., Zhao, S., Meng, Y., Soghier, L., Zhang, X., and Hahn, J. (2020a). A physics-based virtual reality simulation framework for neonatal endotracheal intubation. pages 557–565.

Xiao, X., Zhao, S., Zhang, X., Soghier, L., and Hahn, J. (2020b). Automated assessment of neonatal endotracheal intubation measured by a virtual reality simulation system. pages 2429– 2433.

Xu, W., Ding, H., Zhang, R., and Liang, H. (2020). Estimation and inference in partially func- tional linear regression with multiple functional covariates. Journal of Statistical Planning and Inference, 209:44–61.

Yang, Z., Nguyen, L., Zhu, J., Pan, Z., Li, J., and Jin, F. (2020a). Coordinating disaster emergency response with heuristic reinforcement learning. In 2020 IEEE/ACM International Con- ference on Advances in Social Networks Analysis and Mining (ASONAM), pages 565–572. IEEE.

Yang, Z., Xu, J., Pan, Z., and Jin, F. (2020b). Covid19 tracking: An interactive tracking, visualizing and analyzing platform. In 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 941–943. IEEE.

Zhang, P. and Mahmoud, H. M. (2020). On nodes of small degrees and degree profile in preferential dynamic attachment circuits. Methodology and Computing in Applied Probability, 22(2):625–645.

Zhang, X., Zhong, Q., and Wang, J.-L. (2020a). A new approach to varying-coefficient additive models with longitudinal covariates. Computational Statistics & Data Analysis, 145:106912, 15.

Zhang, X., Zou, G., Liang, H., and Carroll, R. J. (2020b). Parsimonious model averaging with a diverging number of parameters. Journal of the American Statistical Association, 115(530):972– 984.

Zhao, S., Li, W., Zhang, X., Xiao, X., Meng, Y., Philbeck, J., Younes, N., Alahmadi, R., Soghier, L., and Hahn, J. (2020a). Automated assessment system with cross reality for neonatal endotracheal intubation training. pages 738–739.

Zhao, S., Xiao, X., Wang, Q., Zhang, X., Li, W., Soghier, L., and Hahn, J. (2020b). An intelligent augmented reality training framework for neonatal endotracheal intubation. pages 672– 681.

Zhao, S., Xiao, X., Zhang, X., Meng, W. L. Y., Soghier, L., and Hahn, J. K. (2020c). Auto- mated assessment system for neonatal endotracheal intubation using dilated convolutional neural network. pages 5455–5458.

## 2019 Faculty Publications

Amiri, S. and Modarres, R. (2019). Statistical inference of ranked set sampling via resampling methods. pages 59–70. Academic Press, London.

Anderson, T. L., Zhang, X., Martin, S. S., Fang, Y., and Li, J. (2019). Understanding differences in types of opioid prescriptions across time and space: A community-level analysis. Journal of Drug Issues, 49(2):405–418.

Chen, X., Li, H., Liang, H., and Lin, H. (2019). Functional response regression analysis. Journal of Multivariate Analysis, 169:218–233.

Cheung, L. C., Pan, Q., Hyun, N., and Katki, H. A. (2019). Prioritized concordance index for hierarchical survival outcomes. Statistics in Medicine, 38(15):2868–2882.

Cui, X., Zhao, W., Lian, H., and Liang, H. (2019). Pursuit of dynamic structure in quantile additive models with longitudinal data. Computational Statistics & Data Analysis, 130:42–60.

Guo, L. and Modarres, R. (2019). Interpoint distance classification of high dimensional discrete observations. International Statistical Review. Revue Internationale de Statistique, 87(2):191–206.

Huang, H., Shangguan, J., Ruan, P., and Liang, H. (2019a). Bi-level feature selection in high dimensional AFT models with applications to a genomic study. Statistical Applications in Genetics and Molecular Biology, 18(5):20190016, 11.

Huang, H., Tang, Y., Li, Y., and Liang, H. (2019b). Estimation in additive models with fixed censored responses. Journal of Nonparametric Statistics, 31(1):131–143.

Hubbard, A. H., Zhang, X., Jastrebski, S., Singh, A., and Schmidt, C. (2019). Understanding the liver under heat stress with statistical learning: an integrated metabolomics and transcriptomics computational approach. BMC Genomics, 20(1):1–16.

Li, D. and Wang, H. J. (2019). Extreme quantile estimation for autoregressive models. Journal of Business & Economic Statistics, 37(4):661–670.

Li, F., Tang, Y., and Wang, H. J. (2019). Copula-based semiparametric analysis for time series data with detection limits. The Canadian Journal of Statistics, 47(3):438–454.

Lin, H., Lian, H., and Liang, H. (2019a). Rank reduction for high-dimensional generalized additive models. Journal of Multivariate Analysis, 173:672–684.

Lin, H., Yang, B., Zhou, L., Yip, P. S. F., Chen, Y.-Y., and Liang, H. (2019b). Global kernel estimator and test of varying-coefficient autoregressive model. The Canadian Journal of Statistics, 47(3):487–519.

Lu, Y., Hahn, J. K., and Zhang, X. (2019). 3d shape-based body composition inference model using a bayesian network. IEEE Journal of Biomedical and Health Informatics, 24(1):205–213.

Mahmoud, H. M. (2019a). Local and global degree profiles of randomly grown self-similar hooking networks under uniform and preferential attachment. Advances in Applied Mathematics, 111:101930, 25.

Mahmoud, H. M. (2019b). A spectrum of series-parallel graphs with multiple edge evolution. Probability in the Engineering and Informational Sciences, 33(4):487–499.

Mitra, P., Lian, H., Mitra, R., Liang, H., and Xie, M.-g. (2019). A general framework for frequentist model averaging. Science China. Mathematics, 62(2):205–226.

Song, Y. and Modarres, R. (2019a). Interpoint distance test of homogeneity for multivariate mixture models. International Statistical Review. Revue Internationale de Statistique, 87(3):613– 638.

Song, Y. and Modarres, R. (2019b). Unified multivariate hypergeometric interpoint distances. Statistics, 53(4):921–942.

Wang, B., Fang, Y., Lian, H., and Liang, H. (2019a). Additive partially linear models for massive heterogeneous data. Electronic Journal of Statistics, 13(1):391–431.

Wang, H. J., Feng, X., and Dong, C. (2019b). Copula-based quantile regression for longitudinal data. Statistica Sinica, 29(1):245–264.

Wang, Q., Lu, Y., Zhang, X., and Hahn, J. K. (2019c). A novel hybrid model for visceral adipose tissue prediction using shape descriptors. pages 1729–1732.

Wong, R. K. W. and Zhang, X. (2019). Nonparametric operator-regularized covariance function estimation for functional data. Computational Statistics & Data Analysis, 131:131–144.

Xu, W., Zhang, R., and Liang, H. (2019). Two-sample functional linear models. Statistica Sinica, 29(4):1891–1913.

Zhang, X. and Gastwirth, J. L. (2019). Large sample properties of a new measure of income inequality. Statistics & Probability Letters, 145:50–56.

Zhang, Y., Wang, H. J., and Zhu, Z. (2019a). Quantile-regression-based clustering for panel data. Journal of Econometrics, 213(1):54–67.

Zhang, Y., Wang, H. J., and Zhu, Z. (2019b). Robust subgroup identification. Statistica Sinica, 29(4):1873–1889.

Zhao, Y., Pan, Q., and Du, C. (2019). Logistic regression augmented community detection for network data with application in identifying autism-related gene pathways. Biometrics, 75(1):222– 234.

Zhou, L., Lin, H., Chen, K., and Liang, H. (2019). Efficient estimation and computation of parameters and nonparametric functions in generalized semi/non-parametric regression models. Journal of Econometrics, 213(2):593–607.

Zhu, H. and Hu, F. (2019). Sequential monitoring of covariate-adaptive randomized clinical trials. Statistica Sinica, 29(1):265–282.

Miao, W. and Gastwirth, J. L. (2018). Case comment: estimating the economic value of the loss of a chance: a re-examination of chaplin v. hicks. Law, Probability and Risk, 17(4):279–293.

Wang, H., Hueman, M., Pan, Q., Henson, D., Schwartz, A., Sheng, L., and Chen, D. (2018). Creating prognostic systems by the mann-whitney parameter. pages 33–39.