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Publications [Citations] [Software]
| Manuscripts |
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Mei, T., Fan, Y. and Lv, J. (2024). Exogenous randomness empowering random forests. Manuscript. [PDF]
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Huang, J., Du, Y., Kelly, K. R., Fan, Y., Lv, J., Zhong, J. F. and Sun, F. (2024). DeepDeconUQ estimates malignant cell fraction prediction intervals in bulk RNA-seq tissue. Manuscript. [PDF]
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Zheng, Z., Zhou, X., Fan, Y. and Lv, J. (2024). SOFARI: high-dimensional manifold-based inference. Manuscript. [PDF]
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Zuo, W., Zhu, Z., Du, Y., Yeh, Y.-C., Fuhrman, J. A., Lv, J., Fan, Y. and Sun, F. (2024). DeepLINK-T: deep learning inference for time series data using knockoffs and LSTM. Manuscript. [PDF]
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Fan, Y., Gao, L. and Lv, J. (2024). ARK: robust knockoffs inference with coupling. Manuscript. [PDF]
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Fan, J., Fan, Y., Lv, J. and Yang, F. (2024). SIMPLE-RC: group network inference with non-sharp nulls and weak signals. Manuscript. [PDF]
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Chi, C.-M., Fan, Y. and Lv, J. (2024). FACT: high-dimensional multi-output random forests inference. Manuscript. [PDF]
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| 2024 |
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Demirkaya, E., Fan, Y., Gao, L., Lv, J., Vossler, P. and Wang, J. (2024).
Optimal nonparametric inference with two-scale distributional nearest neighbors.
Journal of the American Statistical Association 119, 297-307. [PDF] [Supplementary Material]
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Chi, C.-M., Fan, Y., Ing, C.-K. and Lv, J. (2024).
High-dimensional knockoffs inference for time series data.
Journal of the American Statistical Association, to appear. [PDF]
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| 2022 |
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Fan, J., Fan, Y., Han, X. and Lv, J. (2022).
SIMPLE: statistical inference on membership profiles in large networks.
Journal of the Royal Statistical Society Series B 84, 630-653. [PDF] [Supplementary Material]
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Demirkaya, E., Feng, Y., Basu, P. and Lv, J. (2022).
Large-scale model selection in misspecified generalized linear models.
Biometrika 109, 123-136. [PDF] [Supplementary Material]
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Fan, J., Fan, Y., Han, X. and Lv, J. (2022).
Asymptotic theory of eigenvectors for random matrices with diverging spikes.
Journal of the American Statistical Association 117, 996-1009. [PDF] [Supplementary Material]
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Chi, C.-M., Vossler, P., Fan, Y. and Lv, J. (2022).
Asymptotic properties of high-dimensional random forests.
The Annals of Statistics 50, 3415-3438. [PDF]
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Li, D., Kong, Y., Fan, Y. and Lv, J. (2022).
High-dimensional interaction detection with false sign rate control.
Journal of Business & Economic Statistics 40, 1234-1245. [PDF] [Supplementary Material]
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| 2021 |
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Zheng, Z., Lv, J. and Lin, W. (2021).
Nonsparse learning with latent variables.
Operations Research 69, 346-359. [PDF] [Supplementary Material]
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Zhu, Z., Fan, Y., Kong, Y., Lv, J. and Sun, F. (2021).
DeepLINK: deep learning inference using knockoffs with applications to genomics.
Proceedings of the National Academy of Sciences of the United States of America 118, e2104683118. [PDF]
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Gao, L., Fan, Y., Lv, J. and Shao, Q. (2021).
Asymptotic distributions of high-dimensional distance correlation inference.
The Annals of Statistics 49, 1999-2020. [PDF] [Supplementary Material]
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Huang, N., Mojumder, P., Sun, T., Lv, J. and Golden, J. M. (2021).
Not registered? Please sign-up first: a randomized field experiment on the ex-ante registration request.
Information Systems Research 32, 914-931. [PDF]
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| 2020 |
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Fan, Y., Demirkaya, E., Li, G. and Lv, J. (2020).
RANK: large-scale inference with graphical nonlinear knockoffs.
Journal of the American Statistical Association 115, 362-379. [PDF] [Supplementary Material]
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Fan, Y., Lv, J., Sharifvaghefi, M. and Uematsu, Y. (2020).
IPAD: stable interpretable forecasting with knockoffs inference.
Journal of the American Statistical Association 115, 1822-1834. [PDF] [Supplementary Material]
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Wu, H., Fan, Y. and Lv, J. (2020).
Statistical insights into deep neural network learning in subspace classification.
Stat 9, e273. [PDF]
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| 2019 |
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Fan, Y., Demirkaya, E. and Lv, J. (2019).
Nonuniformity of p-values can occur early in diverging dimensions.
Journal of Machine Learning Research 20, 1-33. [PDF]
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Ren, Z., Kang, Y., Fan, Y. and Lv, J. (2019).
Tuning-free heterogeneous inference in massive networks.
Journal of the American Statistical Association 114, 1908-1925. [PDF] [Supplementary Material]
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Uematsu, Y., Fan, Y., Chen, K., Lv, J. and Lin, W. (2019).
SOFAR: large-scale association network learning.
IEEE Transactions on Information Theory 65, 4924-4939. [PDF] [Supplementary Material]
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Zheng, Z., Bahadori, M. T., Liu, Y. and Lv, J. (2019).
Scalable interpretable multi-response regression via SEED.
Journal of Machine Learning Research 20, 1-34. [PDF] [Supplementary Material]
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| 2018 |
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Candès, E. J., Fan, Y., Janson, L. and Lv, J. (2018).
Panning for gold: 'model-X' knockoffs for high dimensional controlled variable selection.
Journal of the Royal Statistical Society Series B 80, 551-577. [PDF] [Supplementary Material]
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Fan, J. and Lv, J. (2018).
Sure independence screening (invited review article).
Wiley StatsRef: Statistics Reference Online. [PDF]
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Lu, Y., Fan, Y., Lv, J. and Noble, W. S. (2018).
DeepPINK: reproducible feature selection in deep neural networks.
Advances in Neural Information Processing Systems (NeurIPS 2018). [PDF]
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| 2017 |
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Kong, Y., Li, D., Fan, Y. and Lv, J. (2017).
Interaction pursuit in high-dimensional multi-response regression via distance correlation.
The Annals of Statistics 45, 897-922. [PDF] [Supplementary Material]
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Demirkaya, E. and Lv, J. (2017).
Discussion of "Random-projection ensemble classification."
Journal of the Royal Statistical Society Series B 79, 1008-1009. [PDF]
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Lu, Y., Lv, J., Fuhrman, J. A. and Sun, F. (2017).
Towards enhanced and interpretable clustering/classification in integrative genomics.
Nucleic Acids Research 45, e169. [PDF]
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| 2016 |
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Fan, Y. and Lv, J. (2016).
Innovated scalable efficient estimation in ultra-large Gaussian graphical models.
The Annals of Statistics 44, 2098-2126. [PDF] [Supplementary Material]
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Kong, Y., Zheng, Z. and Lv, J. (2016).
The constrained Dantzig selector with enhanced consistency.
Journal of Machine Learning Research 17, 1-22. [PDF]
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Zhang, H., Zheng, Y., Zhang, Z., Gao, T., Joyce, B., Yoon, G., Zhang, W., Schwartz, J., Just, A., Colicino, E., Vokonas, P., Zhao, L., Lv, J., Baccarelli, A., Hou, L. and Liu, L. (2016).
Estimating and testing high-dimensional mediation effects in epigenetic studies.
Bioinformatics 32, 3150-3154. [PDF]
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| 2015 |
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Kim, S., Ogawa, K., Lv, J., Schweighofer, N. and Imamizu, H. (2015).
Neural substrates related to motor memory with multiple timescales in sensorimotor adaptation.
PLOS Biology 13, e1002312. [PDF]
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| 2014 |
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Lv, J. and Liu, J. S. (2014).
Model selection principles in misspecified models.
Journal of the Royal Statistical Society Series B 76, 141-167. [PDF]
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Fan, Y. and Lv, J. (2014).
Asymptotic properties for combined L1 and concave regularization.
Biometrika 101, 57-70. [PDF]
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Zheng, Z., Fan, Y. and Lv, J. (2014).
High dimensional thresholded regression and shrinkage effect.
Journal of the Royal Statistical Society Series B 76, 627-649. [PDF]
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Lv, J. and Zheng, Z. (2014).
Discussion: A significance test for the Lasso.
The Annals of Statistics 42, 493-500. [PDF]
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| 2013 |
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Lv, J. (2013).
Impacts of high dimensionality in finite samples.
The Annals of Statistics 41, 2236-2262. [PDF]
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Fan, Y. and Lv, J. (2013).
Asymptotic equivalence of regularization methods in thresholded parameter space.
Journal of the American Statistical Association 108, 1044-1061. [PDF]
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Lin, W. and Lv, J. (2013).
High-dimensional sparse additive hazards regression.
Journal of the American Statistical Association 108, 247-264. [PDF]
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| 2011 |
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Fan, J. and Lv, J. (2011).
Nonconcave penalized likelihood with NP-dimensionality.
IEEE Transactions on Information Theory 57, 5467-5484. [PDF]
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Fan, J., Lv, J. and Qi, L. (2011).
Sparse high-dimensional models in economics (invited review article).
Annual Review of Economics 3, 291-317. [PDF]
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| 2010 |
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Fan, J. and Lv, J. (2010).
A selective overview of variable selection in high dimensional feature space (invited review article).
Statistica Sinica 20, 101-148. [PDF]
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Fan, J. and Lv, J. (2010).
Comments on: L1-penalization for mixture regression models.
TEST 19, 264-269. [PDF]
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| 2009 |
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Lv, J. and Fan, Y. (2009).
A unified approach to model selection and sparse recovery using regularized least squares.
The Annals of Statistics 37, 3498-3528. [PDF]
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James, G., Radchenko, P. and Lv, J. (2009).
DASSO: connections between the Dantzig selector and Lasso.
Journal of the Royal Statistical Society Series B 71, 127-142. [PDF]
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| 2008 |
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Fan, J. and Lv, J. (2008).
Sure independence screening for ultrahigh dimensional feature space (with discussion).
Journal of the Royal Statistical Society Series B 70, 849-911. [PDF] [Addendum]
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Fan, J. and Lv, J. (2008).
Rejoinder: Sure independence screening for ultrahigh dimensional feature space.
Journal of the Royal Statistical Society Series B 70, 905-908. [PDF]
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Fan, J., Fan, Y. and Lv, J. (2008).
High dimensional covariance matrix estimation using a factor model.
Journal of Econometrics 147, 186-197. [PDF]
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| 2007 |
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Cai, T. and Lv, J. (2007).
Discussion: The Dantzig selector: statistical estimation when p is much larger than n.
The Annals of Statistics 35, 2365-2369. [PDF]
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Fan, J., Fan, Y. and Lv, J. (2007).
Aggregation of nonparametric estimators for volatility matrix.
Journal of Financial Econometrics 5, 321-357. [PDF]
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