Year |
Citation |
Score |
2023 |
Lee ER, Park S, Lee SK, Hong HG. Quantile forward regression for high-dimensional survival data. Lifetime Data Analysis. PMID 37393569 DOI: 10.1007/s10985-023-09603-w |
0.471 |
|
2021 |
Fei Z, Zheng Q, Hong HG, Li Y. Inference for High-Dimensional Censored Quantile Regression. Journal of the American Statistical Association. 118: 898-912. PMID 37309513 DOI: 10.1080/01621459.2021.1957900 |
0.324 |
|
2020 |
Pijyan A, Zheng Q, Hong HG, Li Y. Consistent Estimation of Generalized Linear Models with High Dimensional Predictors via Stepwise Regression. Entropy (Basel, Switzerland). 22. PMID 33286734 DOI: 10.3390/E22090965 |
0.463 |
|
2019 |
Zheng Q, Hong HG, Li Y. Building Generalized Linear Models with Ultrahigh Dimensional Features: A Sequentially Conditional Approach. Biometrics. PMID 31350909 DOI: 10.1111/Biom.13122 |
0.392 |
|
2019 |
Hong HG, Zheng Q, Li Y. Forward regression for Cox models with high-dimensional covariates. Journal of Multivariate Analysis. 173: 268-290. PMID 31007300 DOI: 10.1016/J.Jmva.2019.02.011 |
0.452 |
|
2019 |
Lin H, Hong HG, Yang B, Liu W, Zhang Y, Fan G, Li Y. Nonparametric Time-Varying Coefficient Models for Panel Data Statistics in Biosciences. 11: 548-566. DOI: 10.1007/S12561-019-09248-0 |
0.319 |
|
2017 |
Kang J, Hong HG, Li YI. Partition-based ultrahigh-dimensional variable screening. Biometrika. 104: 785-800. PMID 29643546 DOI: 10.1093/biomet/asx052 |
0.411 |
|
2017 |
Hong HG, Chen X, Christiani DC, Li Y. Integrated powered density: Screening ultrahigh dimensional covariates with survival outcomes. Biometrics. PMID 29120498 DOI: 10.1111/Biom.12820 |
0.377 |
|
2016 |
Cho H, Hong HG, Kim MO. Efficient quantile marginal regression for longitudinal data with dropouts. Biostatistics (Oxford, England). PMID 26951723 DOI: 10.1093/biostatistics/kxw007 |
0.488 |
|
2016 |
Hong HG, Wang L, He X. A data-driven approach to conditional screening of high-dimensional variables Stat. 5: 200-212. DOI: 10.1002/STA4.115 |
0.344 |
|
2015 |
Hong H, Wang C, Lim YS, Douglas J. Efficient Models for Cognitive Diagnosis With Continuous and Mixed-Type Latent Variables. Applied Psychological Measurement. 39: 31-43. PMID 29880992 DOI: 10.1177/0146621614524981 |
0.326 |
|
2015 |
Hong H, Wang C, Lim YS, Douglas J. Efficient Models for Cognitive Diagnosis With Continuous and Mixed-Type Latent Variables Applied Psychological Measurement. 39: 31-43. DOI: 10.1177/0146621614524981 |
0.378 |
|
2013 |
He X, Wang L, Hong HG. Correction: Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data The Annals of Statistics. 41: 2699-2699. DOI: 10.1214/13-Aos1157 |
0.502 |
|
2013 |
He X, Wang L, Hong HG. Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data The Annals of Statistics. 41: 342-369. DOI: 10.1214/13-Aos1087 |
0.544 |
|
2013 |
Hong HG, Zhou J. A multi-index model for quantile regression with ordinal data Journal of Applied Statistics. 40: 1231-1245. DOI: 10.1080/02664763.2013.785489 |
0.537 |
|
2010 |
Hong HG, He X. Prediction of Functional Status for the Elderly Based on a New Ordinal Regression Model Journal of the American Statistical Association. 105: 930-941. DOI: 10.1198/Jasa.2010.Ap08631 |
0.517 |
|
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