| Mental health among otolaryngology resident and attending physicians during the COVID‐19 pandemic: National study AM Civantos, Y Byrnes, C Chang, A Prasad, K Chorath, SK Poonia, ... Head & neck 42 (7), 1597-1609, 2020 | 179 | 2020 |
| Multiple imputation for general missing data patterns in the presence of high-dimensional data Y Deng, C Chang, MS Ido, Q Long Scientific reports 6 (1), 21689, 2016 | 132 | 2016 |
| Mental health among head and neck surgeons in Brazil during the COVID-19 pandemic: a national study AM Civantos, A Bertelli, A Gonçalves, E Getzen, C Chang, Q Long, ... American journal of otolaryngology 41 (6), 102694, 2020 | 81 | 2020 |
| Estimation of covariance matrix via the sparse Cholesky factor with lasso C Chang, RS Tsay Journal of Statistical Planning and Inference 140 (12), 3858-3873, 2010 | 50 | 2010 |
| <? covid19?> Snapshot Impact of COVID-19 on Mental Wellness in Nonphysician Otolaryngology Health Care Workers: A National Study A Prasad, AM Civantos, Y Byrnes, K Chorath, S Poonia, C Chang, ... OTO open 4 (3), 2473974X20948835, 2020 | 42 | 2020 |
| Multiple imputation for analysis of incomplete data in distributed health data networks C Chang, Y Deng, X Jiang, Q Long Nature communications 11 (1), 5467, 2020 | 38 | 2020 |
| Scalable Bayesian variable selection for structured high-dimensional data C Chang, S Kundu, Q Long Biometrics 74 (4), 1372-1382, 2018 | 38 | 2018 |
| Bayesian generalized biclustering analysis via adaptive structured shrinkage Z Li, C Chang, S Kundu, Q Long Biostatistics 21 (3), 610-624, 2020 | 14 | 2020 |
| Generalized bayesian factor analysis for integrative clustering with applications to multi-omics data EJ Min, C Chang, Q Long 2018 IEEE 5th International Conference on Data Science and Advanced …, 2018 | 14 | 2018 |
| Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data Y Zhao, C Chang, M Hannum, J Lee, R Shen Scientific reports 11 (1), 5146, 2021 | 13 | 2021 |
| Knowledge-guided bayesian support vector machine for high-dimensional data with application to analysis of genomics data W Sun, C Chang, Y Zhao, Q Long 2018 IEEE International Conference on Big Data (Big Data), 1484-1493, 2018 | 13 | 2018 |
| Knowledge-guided statistical learning methods for analysis of high-dimensional-omics data in precision oncology Y Zhao, C Chang, Q Long JCO Precision Oncology 3, 1-9, 2019 | 12 | 2019 |
| Bayesian non-linear support vector machine for high-dimensional data with incorporation of graph information on features W Sun, C Chang, Q Long 2019 IEEE International Conference on Big Data (Big Data), 4874-4882, 2019 | 6 | 2019 |
| Split sampling: Expectations, normalisation and rare events JR Birge, C Chang, NG Polson arXiv preprint arXiv:1212.0534, 2012 | 6 | 2012 |
| A Bayesian multiple imputation approach to bivariate functional data with missing components JH Jang, AK Manatunga, C Chang, Q Long Statistics in medicine 40 (22), 4772-4793, 2021 | 5 | 2021 |
| CEDAR: communication efficient distributed analysis for regressions C Chang, Z Bu, Q Long Biometrics 79 (3), 2357-2369, 2023 | 4 | 2023 |
| Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis J Bao, C Chang, Q Zhang, AJ Saykin, L Shen, Q Long, ... Briefings in Bioinformatics 24 (2), bbad073, 2023 | 3 | 2023 |
| Incorporating graph information in Bayesian factor analysis with robust and adaptive shrinkage priors Q Zhang, C Chang, L Shen, Q Long Biometrics 80 (1), ujad014, 2024 | 2 | 2024 |
| Genetic underpinnings of brain structural connectome for young adults Y Zhao, C Chang, J Zhang, Z Zhang Journal of the American Statistical Association 118 (543), 1473-1487, 2023 | 2 | 2023 |
| A Bayesian latent class model to predict kidney obstruction in the absence of gold standard C Chang, JH Jang, A Manatunga, AT Taylor, Q Long Journal of the American Statistical Association 115 (532), 1645-1663, 2020 | 2 | 2020 |
Qi LongProfessor, University of Pennsylvania在 upenn.edu 的电子邮件经过验证
