@article{zhang_li_zhou_zhou_shen_2019, title={TENSOR GENERALIZED ESTIMATING EQUATIONS FOR LONGITUDINAL IMAGING ANALYSIS}, volume={29}, ISSN={["1996-8507"]}, DOI={10.5705/ss.202017.0153}, abstractNote={Longitudinal neuroimaging studies are becoming increasingly prevalent, where brain images are collected on multiple subjects at multiple time points. Analyses of such data are scientifically important, but also challenging. Brain images are in the form of multidimensional arrays, or tensors, which are characterized by both ultrahigh dimensionality and a complex structure. Longitudinally repeated images and induced temporal correlations add a further layer of complexity. Despite some recent efforts, there exist very few solutions for longitudinal imaging analyses. In response to the increasing need to analyze longitudinal imaging data, we propose several tensor generalized estimating equations (GEEs). The proposed GEE approach accounts for intra-subject correlation, and an imposed low-rank structure on the coefficient tensor effectively reduces the dimensionality. We also propose a scalable estimation algorithm, establish the asymptotic properties of the solution to the tensor GEEs, and investigate sparsity regularization for the purpose of region selection. We demonstrate the proposed method using simulations and by analyzing a real data set from the Alzheimer's Disease Neuroimaging Initiative.}, number={4}, journal={STATISTICA SINICA}, author={Zhang, Xiang and Li, Lexin and Zhou, Hua and Zhou, Yeqing and Shen, Dinggang}, year={2019}, month={Oct}, pages={1977–2005} }
 @article{zhang_wilson_2017, title={System Reliability and Component Importance Under Dependence: A Copula Approach}, volume={59}, ISSN={["1537-2723"]}, DOI={10.1080/00401706.2016.1142907}, abstractNote={ABSTRACT System reliability and component importance are of great interest in reliability modeling, especially when the components within the system are dependent. We characterize the influence of dependence structures on system reliability and component importance in coherent systems with discrete marginal distributions. The effects of dependence are captured through copula theory. We extend our framework to coherent multi-state system. Applications of the derived results are demonstrated using a Gaussian copula, which yields simple interpretations. Simulations and two examples are presented to demonstrate the importance of modeling dependence when estimating system reliability and ranking of component importance. Proofs, algorithms, code, and data are provided in supplementary materials available online.}, number={2}, journal={TECHNOMETRICS}, author={Zhang, Xiang and Wilson, Alyson}, year={2017}, pages={215–224} }
 @article{zhang_wu_wang_li_2016, title={A consistent information criterion for support vector machines in diverging model spaces}, volume={17}, journal={Journal of Machine Learning Research}, author={Zhang, X. and Wu, Y. C. and Wang, L. and Li, R. Z.}, year={2016} }
 @article{novick_zhang_yang_2016, title={A new PK equivalence test for a bridging study}, volume={26}, ISSN={["1520-5711"]}, DOI={10.1080/10543406.2016.1148712}, abstractNote={ABSTRACT In a bridging study, the plasma drug concentration–time curve is generally used to assess bioequivalence between the two formulations. Selected pharmacokinetic (PK) parameters including the area under the concentration–time curve, the maximum plasma concentration or peak exposure (Cmax), and drug half-life (T1/2) are compared to ensure comparable bioavailability of the two formulations. Comparability in these PK parameters, however, does not necessarily imply equivalence of the entire concentration–time profile. In this article, we propose an alternative metric of equivalence based on the maximum difference between PK profiles of the two formulations. A test procedure based on Bayesian analysis and accounting for uncertainties in model parameters is developed. Through both theoretical derivation and empirical simulation, it is shown that the new method provides better control over consumer’s risk.}, number={5}, journal={JOURNAL OF BIOPHARMACEUTICAL STATISTICS}, author={Novick, Steven J. and Zhang, Xiang and Yang, Harry}, year={2016}, pages={992–1002} }
 @inproceedings{zhang_brown_shankar_2016, title={Data-driven personas: Constructing archetypal users with clickstreams and user telemetry}, booktitle={34th Annual CHI Conference on Human Factors in Computing Systems, CHI 2016}, author={Zhang, X. and Brown, H. F. and Shankar, A.}, year={2016}, pages={5350–5359} }
 @article{zhang_wu_wang_li_2016, title={Variable selection for support vector machines in moderately high dimensions}, volume={78}, ISSN={["1467-9868"]}, DOI={10.1111/rssb.12100}, abstractNote={Summary}, number={1}, journal={JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY}, author={Zhang, Xiang and Wu, Yichao and Wang, Lan and Li, Runze}, year={2016}, month={Jan}, pages={53–76} }
 @inproceedings{zhang_brown_shankar_2015, title={Data-driven personas: Constructing archetypal users with clickstreams and user telemetry}, booktitle={34th Annual CHI Conference on Human Factors in Computing Systems, CHI 2016}, author={Zhang, X. and Brown, H. F. and Shankar, A.}, year={2015}, pages={5350–5359} }