Publications

In Press

  • Han F, Liu H.  In Press.  Statistical Analysis of Latent Generalized Correlation Matrix Estimation in Transelliptical Distribution. Bernoulli.
  • Ning Y, Zhao T, Liu H.  In Press.  A Likelihood Ratio Framework for High Dimensional Semiparametric Regression. The Annals of Statistics.
  • Ning Y, Liu H.  In Press.  A General Theory of Hypothesis Tests and Confidence Regions for Sparse High Dimensional Models. The Annals of Statistics.
  • Song L, Liu H, Parikh A, Xing E.  In Press.  Nonparametric Latent Tree Graphical Models: Inference, Estimation, and Learning. Journal of Machine Learning Research.
  • Vanderbei R, Liu H, Wang L, Lin K.  In Press.  Optimization for Compressed Sensing: the Simplex Method and Kronecker Sparsification. Mathematical Programming Computation.

2017

  • Liu H, Wang L.  2017.  TIGER: A Tuning-Insensitive Approach for Optimally Estimating Gaussian Graphical Models. Electronic Journal of Statistics.

2016

  • Beatson A, Wang Z, Liu H.  2016.  Blind Attacks on Machine Learners. Advances in Neural Information Processing Systems.
  • Cheng M-Y, Fan J.  2016.  Peter Hall’s contributions to nonparametric function estimation and modeling. The Annals of Statistics. 44:1837–1853.
  • Dobriban E, Fan J.  2016.  Regularity Properties for Sparse Regression. Communications in Mathematics and Statistics. 4:1–19.
  • Fan J, Feng Y, Jiang J, Tong X.  2016.  Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification. Journal of the American Statistical Association. 111:275–287.
  • Fan J, Liu H, Ning Y, Zou H.  2016.  High dimensional semiparametric latent graphical model for mixed data. Journal of the Royal Statistical Society: Series B (Statistical Methodology).
  • Fan J, Imerman M.B., Dai W.  2016.  What does the volatility risk premium say about liquidity provision and demand for hedging tail risk? Journal of Business and Economics Statistics.
  • Fan J, Han F, Liu H, Vickers B.  2016.  Robust inference of risks of large portfolios. Journal of Econometrics. 194(2):298-308.
  • Fan J, Liao Y, Liu H.  2016.  An overview of the estimation of large covariance and precision matrices. The Econometrics Journal. 19:C1–C32.
  • Fan J, Liao Y, Wang W.  2016.  Projected principal component analysis in factor models. Annals of statistics. 44:219.
  • Fan J, Zhou W..  2016.  Guarding from Spurious Discoveries in High Dimension. Journal of Machine Learning Research.
  • Fan J, Furger A., Xiu D..  2016.  Incorporating global industrial classification standard into portfolio allocation: A simple factor-based large covariance matrix estimator with high frequency data. Journal of Business and Economics Statistics.
  • Gu Q, Wang Z, Liu H.  2016.  Low-Rank and Sparse Structure Pursuit via Alternating Minimization. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics.
  • Han F, Liu H, Caffo B.  2016.  Sparse Median Graphs Estimation in a High Dimensional Semiparametric Model. The Annals of Applied Statistics.
  • Han F, Liu H.  2016.  ECA: High Dimensional Elliptical Component Analysis in nonGaussian Distributions. Journal of the American Statistical Association.
  • Kang J, F Bowman DB, Mayberg H, Liu H.  2016.  A depression network of functionally connected regions discovered via multi-attribute canonical correlation graphs. NeuroImage. 141:431–441.
  • Li Y, Liu H, Powell W.  2016.  A Lasso-based Sparse Knowledge Gradient Policy for Sequential Optimal Learning. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics.
  • Li CJunchi, Wang Z, Liu H.  2016.  Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes. Advances in Neural Information Processing Systems.
  • Li X, Zhao T, Arora R, Liu H, Hong M.  2016.  An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics.
  • Li X, Zhao T, Arora R, Liu H, Haupt J.  2016.  Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning. Proceedings of the 33rd International Conference on Machine Learning (ICML-16).
  • Liu H, Mulvey J, Zhao T.  2016.  A semiparametric graphical modelling approach for large-scale equity selection. Quantitative Finance. 16:1053-1067.
  • Neykov M, Wang Z, Liu H.  2016.  Agnostic Estimation for Misspecified Phase Retrieval. Advances in Neural Information Processing Systems.
  • Qiu H, Han F, Liu H, Caffo B.  2016.  Joint estimation of multiple graphical models from high dimensional time series. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 78:487–504.
  • Shou H, Shinohara RT, Liu H, Reich DS, Crainiceanu CM.  2016.  Soft Null Hypotheses: A Case Study of Image Enhancement Detection in Brain Lesions. Journal of Computational and Graphical Statistics. 25:570–588.
  • Sun WWei, Lu J, Liu H, Cheng G.  2016.  Provable sparse tensor decomposition. Journal of the Royal Statistical Society: Series B (Statistical Methodology).
  • TAN KEANMING, Ning Y, WITTEN DANIELAM, Liu H.  2016.  Replicates in high dimensions, with applications to latent variable graphical models. Biometrika. 99(1):1-17.
  • Wang Z, Gu Q, Liu H.  2016.  Statistical Limits of Convex Relaxations. Proceedings of the 33rd International Conference on Machine Learning (ICML-16).
  • Wang M, Fang EX, Liu H.  2016.  Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions. Mathematical Programming. :1–31.
  • Xiao H, Gao J, Wang Z, Wang S, Su L, Liu H.  2016.  A Truth Discovery Approach with Theoretical Guarantee. Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  • Yang Z, Wang Z, Liu H, Eldar YC, Zhang T.  2016.  Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference. Proceedings of the 33rd International Conference on Machine Learning (ICML-16).
  • Yi X, Wang Z, Yang Z, Caramanis C, Liu H.  2016.  More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning. Advances in Neural Information Processing Systems.
  • Zhao T, Cheng G, Liu H.  2016.  A partially linear framework for massive heterogeneous data. The Annals of Statistics. 44:1400–1437.
  • Zhao T, Liu H.  2016.  Accelerated Path-following Iterative Shrinkage Thresholding Algorithm with Application to Semiparametric Graph Estimation. Journal of Computational and Graphical Statistics.

2015

  • Chen L, Liu H, Kocher J-PA, Li H, Chen J.  2015.  glmgraph: an R package for variable selection and predictive modeling of structured genomic data. Bioinformatics. 31:3991–3993.
  • Dette H., Hallin M., Kley T., Volgushev S..  2015.  Of quantiles, ranks, and spectra: an $L_1$ approach to spectral analysis. Bernoulli.
  • Fan J, Tong X, Zeng Y.  2015.  Multi-Agent Inference in Social Networks: A Finite Population Learning Approach. Journal of the American Statistical Association. 110:149–158.
  • Fan J, Rigollet P, Wang W.  2015.  Estimation of functionals of sparse covariance matrices. Annals of statistics. 43:2706.
  • Fan J, Liao Y, Shi X.  2015.  Risks of large portfolios. Journal of econometrics. 186:367–387.
  • Fan J, Ke ZTracy, Liu H, Xia L.  2015.  QUADRO: A supervised dimension reduction method via Rayleigh quotient optimization. Annals of statistics. 43:1498.
  • Fan J, Liao Y, Yao J.  2015.  Power Enhancement in High-Dimensional Cross-Sectional Tests. Econometrica. 83:1497–1541.
  • Fang EX, He B, Liu H, Yuan X.  2015.  Generalized alternating direction method of multipliers: new theoretical insights and applications. Mathematical Programming Computation. 7:149–187.
  • Forni M., Hallin M., Lippi M., Zaffaroni P..  2015.  Dynamic factor models with infinite-dimensional factor space: one-sided representations. Journal of Econometrics.
  • Hallin M., Lu Z., Paindaveine D, Siman M..  2015.  Local bilinear multiple-output quantile regression. Bernoulli.
  • Hallin M., Mehta C..  2015.  R-estimation for asymmetric Independent Component Analysis. Journal of the American Statistical Association.
  • Hallin M., van den Akker R., Werker B..  2015.  On quadratic expansions of log-likelihoods and a general asymptotic linearity result. Mathematical Statistics and Limit Theorems: Festschrift in Honor of Paul Deheuvels.
  • Han F, Lu H, Liu H.  2015.  A direct estimation of high dimensional stationary vector autoregressions. Journal of Machine Learning Research. 16:3115–3150.
  • Hao N, Dong B, Fan J.  2015.  Sparsifying the Fisher linear discriminant by rotation. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 77:827–851.
  • Hörmann S., Kidzinski L., Hallin M..  2015.  Dynamic functional principal components. Journal of the Royal Statistical Society Series B.
  • Ke ZTracy, Fan J, Wu Y.  2015.  Homogeneity pursuit. Journal of the American Statistical Association. 110:175–194.
  • Kolar M, Liu H.  2015.  Optimal feature selection in high-dimensional discriminant analysis. IEEE Transactions on Information Theory. 61:1063–1083.
  • Li X, Zhao T, Yuan X, Liu H.  2015.  An R Package flare for High Dimensional Linear Regression and Precision Matrix Estimation. Journal of Machine Learning Research.
  • Li X, Zhao T, Yuan X, Liu H.  2015.  The flare package for high dimensional linear regression and precision matrix estimation in R. The Journal of Machine Learning Research. 16:553–557.
  • Liu H, Wang L, Zhao T.  2015.  Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery. Journal of Machine Learning Research. 16:1579-1606.
  • Qiu H, Han F, Liu H, Caffo B.  2015.  Robust portfolio optimization. Advances in Neural Information Processing Systems.
  • Qiu H, Xu S, Han F, Liu H, Caffo B.  2015.  Robust estimation of transition matrices in high dimensional heavy-tailed vector autoregressive processes. Proceedings of the 32nd International Conference on Machine Learning (ICML-15).
  • Sun W, Wang Z, Liu H, Cheng G.  2015.  Non-convex statistical optimization for sparse tensor graphical model. Advances in Neural Information Processing Systems.
  • Vainsencher D, Liu H, Zhang T.  2015.  Local smoothness in variance reduced optimization. Advances in Neural Information Processing Systems.
  • Wang Z, Gu Q, Ning Y, Liu H.  2015.  High dimensional em algorithm: Statistical optimization and asymptotic normality. Advances in Neural Information Processing Systems.
  • Yi X, Wang Z, Caramanis C, Liu H.  2015.  Optimal linear estimation under unknown nonlinear transform. Advances in Neural Information Processing Systems.
  • Zhao T, Wang Z, Liu H.  2015.  A nonconvex optimization framework for low rank matrix estimation. Advances in Neural Information Processing Systems.

2014

  • Chaudhuri K., Dasgupta S., Kpotufe S., von Luxburg U.  2014.  Consistent procedures for cluster-tree estimation and pruning.. IEEE Transactions on Information Theory. 60(12):7900-7912.
  • Chen C, Liu H, Metaxas D, Zhao T.  2014.  Mode Estimation for High Dimensional Discrete Tree Graphical Models. Advances in Neural Information Processing Systems. 17:1323--1331.
  • Dasgupta S., Kpotufe S..  2014.  Optimal rates for k-NN density and mode estimation. Neural Information Processing Systems (NIPS).
  • Fan J, Xue L, Zou H.  2014.  Strong oracle optimality of folded concave penalized estimation. The Annals of Statistics. 42:819–849.
  • Fan J, Han F, Liu H.  2014.  Challenges of Big Data Analysis. National Science Review. 2(1):1-24.
  • Fan J, Fan Y, Barut E.  2014.  Adaptive robust variable selection. The Annals of Statistics. 42:324–351.
  • Fan J, Ma Y, Dai W.  2014.  Nonparametric independence screening in sparse ultra-high dimensional varying coefficient models. Journal of the American Statistical Association. 109:1270-1284.
  • Fan J, Qi L, Xiu D.  2014.  Quasi-Maximum Likelihood Estimation of GARCH Models With Heavy-Tailed Likelihoods. Journal of Business & Economic Statistics. 32:178–191.
  • Fan J, Liao Y.  2014.  Endogeneity in high dimensions. The Annals of Statistics. 42:872–917.
  • Gu Q, Wang Z, Liu H.  2014.  Oracle Sparse PCA and Its Inference. Advances in Neural Information Processing Systems. 17:1529-1537.
  • Hallin M., Ley C..  2014.  Skew-symmetric distributions and Fisher information: the double sin of the skew-normal. Bernoulli. 20:1432-1453.
  • Hallin M., Paindaveine D, Verdebout T.  2014.  Efficient R-estimation of principal and common principal components. Journal of the American Statistical Association. 109:1071-1083.
  • Han F, Liu H.  2014.  Scale-Invariant Sparse PCA on High Dimensional Meta-elliptical Data. Journal of American Statistical Association. 109(505):275-287.
  • Han F, Liu H.  2014.  High dimensional semiparametric scale-invariant principal component analysis. IEEE transactions on pattern analysis and machine intelligence. 36:2016–2032.
  • He B, Liu H, Wang Z, Yuan X.  2014.   A Strictly Contractive Peaceman-Rachford Splitting Method for Convex Program. SIAM Journal on Optimization. 24(3):1011-1040.
  • Jiang X, Yao Y, Liu H, Guibas L.  2014.  Compressive Network Analysis. IEEE Transactions on Automatic Control. 59(11):2946-2961.
  • Ke ZT, Jin J, Fan J.  2014.  Covariance assisted screening and estimation. Annals of Statistics. 42:2202-2242.
  • Kolar M, Liu H, Xing E.  2014.  Graph Estimation From Multi-attribute Data. Journal of Machine Learning Research. 15:1713-1750.
  • Kpotufe S., Sgouritsa E., Janzing D., Shoelkopf B..  2014.  Consistency of Causal Inference under the Additive Noise Model. . International Conference on Machine Learning (ICML).
  • Liu H, Wang L, Zhao T.  2014.  Multivariate Regression with Calibration. Advances in Neural Information Processing Systems. 17:127-135.
  • Liu H, Wang L, Zhao T.  2014.  Sparse Covariance Matrix Estimation with Eigenvalue Constraints. Journal of Computational and Graphical Statistics. 23(2):439-459.
  • Onatski A., Moreira M., Hallin M..  2014.  Signal detection in high dimension: the multispiked case. Annals of Statistics. 42:225-254.
  • Pang H, Liu H, Vanderbei R.  2014.  The fastclime Package for Linear Programming and Large-Scale Precision Matrix Estimation. Journal of Machine Learning Research. 15:489-493.
  • Rosenblum M, Liu H, Yen E-H.  2014.  Optimal Tests of Treatment Effects for the Overall Population and Two Subpopulations in Randomized Trials, using Sparse Linear Programming. Journal of American Statistical Association. 109(507):1216-1228.
  • Trivedi S., Wang J., Kpotufe S., Shakhnarovich G..  2014.  A Consistent Estimator of the Expected Gradient Outerproduct. . Uncertainty in Artificial Intelligence (UAI).
  • Wang Z, Liu H.  2014.  Tightening after Relax: Minimax-Optimal Sparse PCA in Polynomial Time. Advances in Neural Information Processing Systems. 17:3383-3391.
  • Wang Z, Liu H, Zhang T.  2014.  Optimal Computational and Statistical Rates of Convergence for Sparse Nonconvex Learning Problems. The Annals of Statistics,. 42(2):2164-2201.
  • Zhao T, Liu H.  2014.  Calibrated Precision Matrix Estimation for High Dimensional Elliptical Distributions. IEEE Transactions on Information Theory. 60(12):7874-7887.
  • Zhao T, Yu M, Wang Y, Arora R, Liu H.  2014.  Accelerated Mini-batch Randomized Block Coordinate Descent Method. Advances in Neural Information Processing Systems. 17:3329--3337.
  • Zhao T, Roeder K, Liu H.  2014.  Positive Semidefinite Rank-based Correlation Matrix Estimation with Application to Semiparametric Graph Estimation. Journal of Computational and Graphical Statistics,. 23(4):895-922.
  • Zhu H, Fan J, Kong L.  2014.  Spatially varying coefficient model for neuroimaging data with jump discontinuities. Journal of American Statistical Association. 109:1084--1098.

2013

  • Aït-Sahalia Y, Fan J, Li Y.  2013.  The leverage effect puzzle: Disentangling sources of bias at high frequency. Journal of Financial Economics. 109:224-249.
  • Audibert JY, Bubeck S, Lugosi G.  2013.  Regret in Online Combinatorial Optimization. Mathematics of Operations Research. (To appear)
  • Berthet A., Rigollet P.  2013.  Complexity Theoretic Lower Bounds for Sparse Principal Component Detection. J. Mach. Learn. Res., W&CP. 30:1046-1066(electronic).
  • Berthet Q, Rigollet P.  2013.  Optimal detection of sparse principal components in high dimension. Ann. Statist.. 41:1780-1815.
  • Bubeck S., Perchet V., Rigollet P.  2013.  Bounded regret in stochastic multi-armed bandits. J. Mach. Learn. Res., W&CP. 30:122-134(electronic).
  • Bubeck S, Liu C.Y..  2013.  Prior-free and prior-dependent regret bounds for Thompson Sampling. Advances in Neural Information Processing Systems.
  • Bubeck S, Wang T, Viswanathan N.  2013.  Multiple Identifications in Multi-Armed Bandits. International Conference on Machine Learning (ICML). 30th
  • Bubeck S, Ernst D, Garivier A.  2013.  Optimal Discovery with Probabilistic Expert Advice: Finite Time Analysis and Macroscopic Optimality. Journal of Machine Learning Research. 14
  • Cai T, Fan J, Jiang T.  2013.  Distributions of Angles in Random Packing on Spheres. Journal of Machine Learning Research. 14:1837-1864.
  • Fan J, Liao Y, Mincheva M.  2013.  Large covariance estimation by thresholding principal orthogonal complements (with discussion). Journal of Royal Statistical Society, B. 75:603-680.
  • Fan J, Maity A, Wang Y, Wu Y.  2013.  Parametrically guided generalised additive models with application to mergers and acquisitions data. Journal of nonparametric statistics. 25:109–128.
  • Fan J, Liu H.  2013.  Statistical Analysis of Big Data on Pharmacogenomics. Advanced drug delivery reviews. 65:987-1000.
  • Hallin M, Lu Z.  2013.  Discussion of “local quantile regression” by Spokoiny, Wang, and Härdle. Journal of Statistical Planning and Inference.
  • Hallin M, Lippi M.  2013.  Factor models in high-dimensional time series a time-domain approach. Stochastic Processes and their Applications.
  • Han F, Liu H.  2013.  Transition Matrix Estimation in High Dimensional Vector Autoregressive Models. Journal of Machine Learning Research (ICML Track). WCP 28(1):pp73-81.
  • Han F, Liu H.  2013.  CODA: Copula Discriminant Analysis. Journal of Machine Learning Research. 14: pp 629-671
  • Han F, Liu H.  2013.  Robust Sparse Principal Component Regression. Neural Information Processing Systems (NIPS). 26
  • Han F, Liu H.  2013.  Principal Componenet Analysis on non-Gaussian Dependent Data. Journal of Machine Learning Research (ICML Track). WCP 28(1):pp240-248..
  • Kolar M, Liu H.  2013.  Feature Selection in High-Dimensional Classification. Journal of Machine Learning Research (ICML Track). WCP 28(1):pp329-337.
  • Ning Y, Liu H.  2013.  High Dimensional Semiparametric Bigraphical Model. Biometrika. 100(3):pp655-670.
  • Onatski A, Moreira MJ, Hallin M.  2013.  Asymptotic power of sphericity tests for high-dimensional data. The Annals of Statistics. 41:1204–1231.
  • Perchet V., Rigollet P.  2013.  The multi-armed bandit problem with covariates. Ann. Statist.. 43:693-721.
  • Wang J, Zhu H, Fan J, Giovanello K, Lin WL.  2013.  Multiscale adaptive smoothing model for the hemodynamic response function in fMRI. Annals of Applied Statistics. 7:904-935.
  • Wang Z, Han F, Liu H.  2013.  Sparse Principal Component Analysis for High Dimensional Multivariate Time Series. Journal of Machine Learning Research, WCP. 31:pp48–56.
  • Zhao T, Liu H.  2013.  Sparse Inverse Covariance Estimation with Calibration. Neural Information Processing Systems (NIPS). 26

2012

  • Arias-Castro E, Bubeck S, Lugosi G.  2012.  Detection of correlations. Annals of Statistics. 40:412–435.
  • Bennala N, Hallin M, Paindaveine D.  2012.  Pseudo-Gaussian and rank-based optimal tests for random individual effects in large small panels. Journal of Econometrics. 170:50-67.
  • Bradic J, Fan J, Jiang J.  2012.  Regularization for Cox’s proportional hazards model with NP-dimensionality. The Annals of Statistics. 39:3092–3120.
  • Bubeck S, Meila M, von Luxburg U.  2012.  How the Initialization Affects the Stability of the k-means Algorithm. ESAIM: Probability and Statistics. 16:436–452.
  • Bubeck S, Cesa-Bianchi N, Kakade SM.  2012.  Towards minimax policies for online linear optimization with bandit feedback. Proceedings of the 25th Annual Conference on Learning Theory (COLT).
  • Bubeck S, Slivkins A.  2012.  The best of both worlds: stochastic and adversarial bandits. Proceedings of the 25th Annual Conference on Learning Theory (COLT).
  • Bubeck S, Ernst D, Garivier A.  2012.  Optimal discovery with probabilistic expert advice. Proceedings of the 51st IEEE Conference on Decision and Control (CDC).
  • Bubeck S, Cesa-Bianchi N.  2012.  Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems. Foundations and Trends in Machine Learning. 5:122.
  • Chen X, Liu H.  2012.  Structured Sparse Canonical Correlation Analysis. Journal of Machine Learning Research (AISTATS track). WCP Vol. 22:199-207.
  • Chen X, Liu H.  2012.  An Efficient Optimization Algorithm for Structured Sparse CCA, with Applications to eQTL Mapping. Statistics in Biosciences. 4(1):3-26.
  • Dai D, Rigollet P, Zhang T.  2012.  Deviation Optimal Learning using Greedy Q-aggregation. Ann. Statist.. 40:1878-1905.
  • Eloyan A, Muschelli J, Nebel M, Liu H, Han F, Zhao T, Caffo B.  2012.  Automated Diagnoses of Attention Deficit Hyperactive Disorder using Magnetic Resonance Image. Frontiers in Systems Neuroscience. 6(61)
  • Fan J, Feng Y, Tong X.  2012.  A ROAD to classification in high dimensional space: the regularized optimal affine discriminant. Journal of the Royal Statistical Society: Series B (Statistical Methodology).
  • Fan J, Han X, Gu W.  2012.  Estimating false discovery proportion under arbitrary covariance dependence (with discussion). Journal of the American Statistical Association. 107:1019–1048.
  • Fan J, Li Y, Yu K.  2012.  Vast Volatility Matrix Estimation Using High-Frequency Data for Portfolio Selection. Journal of the American Statistical Association. 107:412-428.
  • Fan J, Zhang J, Yu K.  2012.  Vast Portfolio Selection With Gross-Exposure Constraints. Journal of the American Statistical Association. 107:592–606.
  • Fan J, Guo S, Hao N.  2012.  Variance estimation using refitted cross-validation in ultrahigh dimensional regression. J. R. Stat. Soc. Ser. B. Stat. Methodol.. 74:37–65.
  • Hallin M.  2012.  Permutation Tests (with C. Ley), Equivariant Estimation (with J. Jureckova). Encyclopedia of Environmetrics, 2nd edition. :209-210,1944-1949.
  • Hallin M, Paindaveine D, Verdebout T.  2012.  Optimal rank-based tests for common principal components. Bernoulli. in press.
  • Hallin M, Ley C.  2012.  Skew-symmetric distributions and Fisher information – a tale of two densities.. Bernoulli. 18:747-763.
  • Hallin M, Swan Y, Verdebout T, Veredas D.  2012.  One-step R-estimation in linear models with stable errors. Journal of Econometrics. :-.
  • Hallin M.  2012.  Exponential Families, Kronecker Product, Asymptotic Relative Efficiency, Multinomial distribution, Gauss-Markov Theorem, Neyman-Pearson Lemma, Binomial distribution, Normal and Multinormal distribution, Poisson distribution, Bartlett test, Ranks. Encyclopedia of Environmetrics, 2nd edition. :106-110,174-179,932-936,1113-1116,1298-1301,1429-1431,1654-1655,1779-1782,1812-1814,1944-1949,2135-2152.
  • Hallin M.  2012.  Principal Components (with S. Hoermann), Hotelling’s T2 tests (robust versions of) (with S. van Aelst). Encyclopedia of Environmetrics, 2nd edition. :1987-1988,910-915.
  • Han F, Liu H.  2012.  Transelliptical Principal Component Analysis for non-Gaussian Data. Advances in Neural Information Processing Systems (NIPS). Volume 25.
  • Han F, Liu H.  2012.  High Dimensional Semiparametric Scale-invariant Principal Component Analysis. Advances in Neural Information Processing Systems (NIPS). Volume 25.
  • Jiang X, Yao Y, Liu H, Guibas L.  2012.  Network Clique Detection using Radon Basis Pursuit. Journal of Machine Learning Research (AISTATS track). WCP Vol. 22:565-573.
  • Kolar M, Liu H.  2012.  Marginal Regression For Multitask Learning. Journal of Machine Learning Research (AISTATS track). WCP Vol. 22:647-655.
  • Lafferty J, Liu H, Wasserman L.  2012.  Sparse Nonparametric Graphical Models. Statistical Science. 27(4):519-537.
  • Liu H, Han F, Yuan M, Lafferty J, Wasserman L.  2012.  The Nonparanormal SKEPTIC. International Conference on Machine Learning (ICML). 29th
  • Liu H, Lafferty J, Wasserman L.  2012.  Exponential Concentration Inequality for Mutual Information Estimation. Advances in Neural Information Processing Systems (NIPS). Volume 25.
  • Liu H, Han F, Yuan M, Lafferty J, Wasserman L.  2012.  High Dimensional Semiparametric Gaussian Copula Graphical Models. Annals of Statistics. 40(40):2293-2326.
  • Liu H, Han F, Zhang C-hui.  2012.  High Dimensional Transelliptical Graphical Models. Neural Information Processing Systems (NIPS).
  • Neal B, Liu H, Zhao T, Roeder K, etc..  2012.  Patterns and Rates of Exonic de novo Mutations in Autism Spectrum Disorders. Nature. 485:242-245.
  • Rigollet P, Tsybakov A.  2012.  Estimation of Covariance Matrices under Sparsity Constraints. Statist. Sinica. 22:1319-1378.
  • Rigollet P, Tsybakov A.  2012.  Sparse estimation by exponential weighting. Statistical Science. 27:558-575.
  • Rigollet P.  2012.  Kullback–Leibler aggregation and misspecified generalized linear models. Ann. Statist.. 40:639-665.
  • Wang X, Chen T, Leng L, Fan J, Cao K, Duan Z, Zhang X, Shao C, Wu M, Tadmori I et al..  2012.  MIF Produced by Bone Marrow–Derived Macrophages Contributes to Teratoma Progression after Embryonic Stem Cell Transplantation. Cancer research. 72:2867–2878.
  • Zhao T, Liu H.  2012.  Sparse Additive Machine. Journal of Machine Learning Research (AISTATS track). WCP Vol. 22:1435-1443.
  • Zhao T, Liu H, Roeder K, Lafferty J, Wasserman L.  2012.  HUGE: High Dimensional Undirected Graph Estimation. Journal of Machine Learning Research. 3:1059-1062.
  • Zhao T, Roeder K, Liu H.  2012.  Nonparanormal Graph Estimation via Smooth-projected Neighborhood Pursuit. Advances in Neural Information Processing Systems (NIPS). Volume 25.

2011

  • Audibert J-Y, Bubeck S, Lugosi G.  2011.  Minimax Policies for Combinatorial Prediction Games. Proceedings of the 24th Annual Conference on Learning Theory (COLT).
  • Audibert J-Y, Bubeck S, Munos R.  2011.  Bandit View on Noisy Optimization. Optimization for Machine Learning. :431–454.
  • Bradic J, Fan J, Jiang J.  2011.  Regularization for Cox’s proportional hazards model with NP-dimensionality. Annals of statistics. 39:3092-3120.
  • Bradic J, Fan J, Wang W.  2011.  Penalized composite quasi-likelihood for ultrahigh dimensional variable selection. J. R. Stat. Soc. Ser. B Stat. Methodol.. 73:325–349.
  • Bubeck S, Munos R, Stoltz G.  2011.  Pure Exploration in Finitely-Armed and Continuously-Armed Bandits. Theoretical Computer Science. 412:1832–1852.
  • Bubeck S, Munos R, Stoltz G, Szepesvari C.  2011.  X-Armed Bandits. Journal of Machine Learning Research. 12:1587–1627.
  • Bubeck S, Stoltz G, Yu JY.  2011.  Lipschitz Bandits without the Lipschitz Constant. Proceedings of the 22nd International Conference on Algorithmic Learning Theory (ALT).
  • Cassart D, Hallin M, Paindaveine D.  2011.  A class of optimal tests for symmetry based on local Edgeworth approximations. Bernoulli. 17:1063–1094.
  • Fan Y, Fan J.  2011.  Testing and detecting jumps based on a discretely observed process. Journal of Econometrics.
  • Fan J, Lv J, Qi L.  2011.  Sparse high dimensional models in economics. Annual Review of Economics. 3:291.
  • Fan J, Liao Y, Mincheva M.  2011.  High Dimensional Covariance Matrix Estimation in Approximate Factor Models..
  • Fan J, Lv J.  2011.  Nonconcave Penalized Likelihood With NP-Dimensionality. Information Theory, IEEE Transactions on. 57:5467–5484.
  • Fan J, Feng Y, Song R.  2011.  Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models. Journal of the American Statistical Association. 106:544–557.
  • Fan J, Liao Y, Mincheva M.  2011.  High-dimensional covariance matrix estimation in approximate factor models. Ann. Statist.. 39:3320–3356.
  • Gabillon V, Ghavamzadeh M, Lazaric A, Bubeck S.  2011.  Multi-Bandit Best Arm Identification. Advances in Neural Information Processing Systems (NIPS).
  • Hallin M, van den Akker R, Werker BJM.  2011.  A class of simple distribution-free rank-based unit root tests. J. Econometrics. 163:200–214.
  • Hallin M, Mathias C, Pirotte H, Veredas D.  2011.  Market liquidity as dynamic factors. J. Econometrics. 163:42–50.
  • Hallin M, Swan Y, Verdebout T, Veredas D.  2011.  Rank-based testing in linear models with stable errors. J. Nonparametr. Stat.. 23:305–320.
  • Hallin M.  2011.  On the non-Gaussian asymptotics of the likelihood ratio test statistic for homogeneity of covariance. Nonparametric statistics and mixture models: A Festschrift in Honor of Thomas P. Hettmansperger. :136–146.
  • Hallin M, Liška R.  2011.  Dynamic factors in the presence of blocks. J. Econometrics. 163:29–41.
  • McKeague IW, López-Pintado S, Hallin M, Šiman M.  2011.  Analyzing growth trajectories. Journal of Developmental Origins of Health and Disease. 2:322-329.
  • Porwancher RB, Hagerty CG, Fan J, Landsberg L, Johnson BJ, Kopnitsky M, Steere AC, Kulas K, Wong SJ.  2011.  Multiplex immunoassay for Lyme disease using VlsE1-IgG and pepC10-IgM antibodies: improving test performance through bioinformatics. Clin. Vaccine Immunol.. 18:851–859.
  • Porwancher RB, C Hagerty G, Fan J, Landsberg L, Johnson BJB, Kopnitsky M, Steere AC, Kulas K, Wong SJ.  2011.  Multiplex immunoassay for Lyme disease using VlsE1-IgG and pepC10-IgM antibodies: improving test performance through bioinformatics. Clinical and Vaccine Immunology. 18:851–859.
  • Rigollet P, Tong X.  2011.  Neyman-Pearson classification, convexity and stochastic constraints. J. Mach. Learn. Res.. 12:2831-2855(electronic).
  • Rigollet P, Tsybakov A.  2011.  Exponential Screening and optimal rates of sparse estimation. Ann. Statist.. 39:731–771.
  • Zhang C, Fan J, Yu T.  2011.  Multiple testing via FDR_L for large-scale imaging data. Ann. Statist.. 39:613–642.

2010

  • Aït-Sahalia Y, Fan J, Xiu D.  2010.  High-frequency covariance estimates with noisy and asynchronous financial data. J. Amer. Statist. Assoc.. 105:1504–1517.
  • Aït-Sahalia Y, Fan J, Jiang J.  2010.  Nonparametric tests of the Markov hypothesis in continuous-time models. Ann. Statist.. 38:3129–3163.
  • Audibert J-Y, Bubeck S.  2010.  Regret Bounds and Minimax Policies under Partial Monitoring. Journal of Machine Learning Research. 11:2635-2686.
  • Audibert J-Y, Bubeck S, Munos R.  2010.  Best Arm Identification in Multi-Armed Bandits. Proceedings of the 23rd Annual Conference on Learning Theory (COLT).
  • Bubeck S, Munos R.  2010.  Open Loop Optimistic Planning. Proceedings of the 23rd Annual Conference on Learning Theory (COLT).
  • Cassart D, Hallin M, Paindaveine D.  2010.  On the estimation of cross-information quantities in rank-based inference. {Nonparametrics and robustness in modern statistical inference and time series analysis: a Festschrift in honor of Professor Jana Jurecková. 7:35–45.
  • Fan J, Lv J.  2010.  Comments on: l_1-penalization for mixture regression models. TEST. 19:264–269.
  • Fan J, Zhang J-T, Zhang W.  2010.  Comments on: Dynamic relations for sparsely sampled Gaussian processes. TEST. 19:37–42.
  • Fan J, Lv J.  2010.  A selective overview of variable selection in high dimensional feature space. Statist. Sinica. 20:101–148.
  • Fan J, Song R.  2010.  Sure independence screening in generalized linear models with NP-dimensionality. Ann. Statist.. 38:3567–3604.
  • Fan J, Feng Y, Niu YS.  2010.  Nonparametric estimation of genewise variance for microarray data. Ann. Statist.. 38:2723–2750.
  • Fan J, Feng Y, Wu Y.  2010.  High-dimensional variable selection for Cox’s proportional hazards model. :70–86.
  • Hallin M, Paindaveine D, Šiman M.  2010.  Multivariate quantiles and multiple-output regression quantiles: from {$L_1$} optimization to halfspace depth. Ann. Statist.. 38:635–669.
  • Hallin M, Paindaveine D, Verdebout T.  2010.  Testing for common principal components under heterokurticity. J. Nonparametr. Stat.. 22:879–895.
  • Hallin M, Paindaveine D, Šiman M.  2010.  Rejoinder [MR2604670; MR2604671; MR2604672; MR2604673]. Ann. Statist.. 38:694–703.
  • Hallin M, Paindaveine D, Verdebout T.  2010.  Optimal rank-based testing for principal components. Ann. Statist.. 38:3245–3299.
  • Hirukawa J, Taniai H, Hallin M, Taniguchi M.  2010.  Rank-based inference for multivariate nonlinear and long-memory time series models. J. Japan Statist. Soc.. 40:167–187.
  • Jiang J, Fan Y, Fan J.  2010.  Estimation in additive models with highly or nonhighly correlated covariates. Ann. Statist.. 38:1403–1432.
  • MAQC C.  2010.  The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat. Biotechnol.. 28:827–838.
  • Shiohama T, Hallin M, Veredas D, Taniguchi M.  2010.  Dynamic portfolio optimization using generalized dynamic conditional heteroskedastic factor models. J. Japan Statist. Soc.. 40:145–166.
  • Sun X, Wang X, Chen T, Li T, Cao K, Lu A, Chen Y, Sun D, Luo J, Fan J.  2010.  Myelin activates FAK/Akt/NF-$ąppa$B pathways and provokes CR3-Dependent inflammatory response in murine system. PloS one. 5:e9380.
  • Wu Y, Fan J, Müller H-G.  2010.  Varying-coefficient functional linear regression. Bernoulli. 16:730–758.

2009

  • Aït-Sahalia Y, Fan J, Peng H.  2009.  Nonparametric transition-based tests for jump diffusions. J. Amer. Statist. Assoc.. 104:1102–1116.
  • Audibert J-Y, Bubeck S.  2009.  Minimax Policies for Adversarial and Stochastic Bandits. Proceedings of the 22nd Annual Conference on Learning Theory (COLT).
  • Bubeck S, von Luxburg U.  2009.  Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions. Journal of Machine Learning Research. 10:657–698.
  • Bubeck S, Munos R, Stoltz G, Szepesvari C..  2009.  Online Optimization in X-Armed Bandits. Advances in Neural Information Processing Systems (NIPS).
  • Bubeck S, Munos R, Stoltz G.  2009.  Pure Exploration in Multi-Armed Bandits Problems. Proceedings of the 20th International Conference on Algorithmic Learning Theory (ALT).
  • Carroll RJ, Delaigle A, Hall P.  2009.  Nonparametric prediction in measurement error models. J. Amer. Statist. Assoc.. 104:993–1003.
  • Delaigle A, Fan J, Carroll RJ.  2009.  A design-adaptive local polynomial estimator for the errors-in-variables problem. J. Amer. Statist. Assoc.. 104:348–359.
  • Fan J, Samworth R, Wu Y.  2009.  Ultrahigh dimensional feature selection: beyond the linear model. J. Mach. Learn. Res.. 10:2013–2038.
  • Fan J, Wu Y, Feng Y.  2009.  Local quasi-likelihood with a parametric guide. Ann. Statist.. 37:4153–4183.
  • Fan J, Lin X, Liu JS.  2009.  New developments in biostatistics and bioinformatics. 1
  • Fan J, Zhou Y, Cai JW, Chen M.  2009.  Gaining efficiency via weighted estimators for multivariate failure time data. Sci. China Ser. A. 52:1113–1138.
  • Fan J, Mancini L.  2009.  Option pricing with model-guided nonparametric methods. J. Amer. Statist. Assoc.. 104:1351–1372.
  • Fan J, Feng Y, Wu Y.  2009.  Network exploration via the adaptive lasso and SCAD penalties. Ann. Appl. Stat.. 3:521–541.
  • Fan J, Feng Y.  2009.  Comments on: Nonparametric prediction in measurement error models . J. Amer. Statist. Assoc.. 104:1003–1007.
  • Fan J, Peng L, Yao Q, Zhang W.  2009.  Approximating conditional density functions using dimension reduction. Acta Math. Appl. Sin. Engl. Ser.. 25:445–456.
  • Fan J, Jiang J.  2009.  Non- and semi-parametric modeling in survival analysis. New developments in biostatistics and bioinformatics. 1:3–33.
  • Hallin M, Paindaveine D.  2009.  Optimal tests for homogeneity of covariance, scale, and shape. J. Multivariate Anal.. 100:422–444.
  • Hallin M, Lu Z, Yu K.  2009.  Local linear spatial quantile regression. Bernoulli. 15:659–686.
  • Lam C, Fan J.  2009.  Sparsistency and rates of convergence in large covariance matrix estimation. Ann. Statist.. 37:4254–4278.
  • Xie Y, Fan J, Chen J, Huang FP, Cao B, Tam PK, Ren Y.  2009.  A novel function for dendritic cell: clearance of VEGF via VEGF receptor-1. Biochem. Biophys. Res. Commun.. 380:243–248.
  • Yap J S, Fan J, Wu R.  2009.  Nonparametric modeling of longitudinal covariance structure in functional mappings of quantitative trait loci. Biometrics. 65:1068–1077.
  • Zhang W, Fan J, Sun Y.  2009.  A semiparametric model for cluster data. Ann. Statist.. 37:2377–2408.

2008

  • Cai JW, Fan J, Jiang J, Zhou H.  2008.  Partially linear hazard regression with varying coefficients for multivariate survival data. J. R. Stat. Soc. Ser. B Stat. Methodol.. 70:141–158.
  • Chen K, Fan J, Jin Z.  2008.  Design-adaptive minimax local linear regression for longitudinal/clustered data. Statist. Sinica. 18:515–534.
  • Fan J, Lv J.  2008.  Sure independence screening for ultrahigh dimensional feature space. J. R. Stat. Soc. Ser. B Stat. Methodol.. 70:849–911.
  • Fan J, Wang Y.  2008.  Spot volatility estimation for high-frequency data. Stat. Interface. 1:279–288.
  • Fan J, Fan Y.  2008.  High-dimensional classification using features annealed independence rules. Ann. Statist.. 36:2605–2637.
  • Fan J, Fan Y, Lv J.  2008.  High dimensional covariance matrix estimation using a factor model. J. Econometrics. 147:186–197.
  • Fan J, Wang M, Yao Q.  2008.  Modelling multivariate volatilities via conditionally uncorrelated components. J. R. Stat. Soc. Ser. B Stat. Methodol.. 70:679–702.
  • Fan J, Zhang W.  2008.  Statistical methods with varying coefficient models. Stat. Interface. 1:179–195.
  • Fan J, Wu Y.  2008.  Semiparametric estimation of covariance matrices for longitudinal data. J. Amer. Statist. Assoc.. 103:1520–1533.
  • Fan J, Mykland P, Yao Q.  2008.  FERM-editorial. Stat. Interface. 1:209.
  • Kim BR, Zhang L, Berg A, Fan J, Wu R.  2008.  A computational approach to the functional clustering of periodic gene-expression profiles. Genetics. 180:821–834.
  • Lam C, Fan J.  2008.  Profile-kernel likelihood inference with diverging number of parameters. Ann. Statist.. 36:2232–2260.
  • Ren Y, Xie Y, Jiang G, Fan J, Yeung J, Li W, Tam PKH, Savill J.  2008.  Apoptotic cells protect mice against lipopolysaccharide-induced shock. The Journal of Immunology. 180:4978–4985.
  • von Luxburg U, Bubeck S, Jegelka S, Kaufmann M.  2008.  Consistent Minimization of Clustering Objective Functions. Advances in Neural Information Processing Systems (NIPS).

2007

  • Cai JW, Fan J, Zhou H, Zhou Y.  2007.  Hazard models with varying coefficients for multivariate failure time data. Ann. Statist.. 35:324–354.
  • Cai JW, Fan J, Jiang J, Zhou H.  2007.  Partially linear hazard regression for multivariate survival data. J. Amer. Statist. Assoc.. 102:538–551.
  • Fan J, Hall P, Yao Q.  2007.  To how many simultaneous hypothesis tests can normal, Student's t or bootstrap calibration be applied? J. Amer. Statist. Assoc.. 102:1282–1288.
  • Fan J.  2007.  Variable screening in high-dimensional feature space. Proceedings of the 4th International Congress of Chinese Mathematicians. II:735-747.
  • Fan J, Jiang J.  2007.  Rejoinder on: Nonparametic inference with generalized likelihood ratio tests. TEST. 16:471–478.
  • Fan J, Niu Y.  2007.  Selection and validation of normalization methods for c-DNA microarrays using within-array replications. Bioinformatics. 23:2391–2398.
  • Fan J, Huang T, Li R.  2007.  Analysis of longitudinal data with semiparametric estimation of covariance function. J. Amer. Statist. Assoc.. 102:632–641.
  • Fan J, Wang Y.  2007.  Multi-scale jump and volatility analysis for high-frequency financial data. J. Amer. Statist. Assoc.. 102:1349–1362.
  • Fan J, Jiang J.  2007.  Nonparametric inference with generalized likelihood ratio tests. TEST. 16:409–444.
  • Fan J, Fan Y, Jiang J.  2007.  Dynamic integration of time- and state-domain methods for volatility estimation. J. Amer. Statist. Assoc.. 102:618–631.
  • Fan J, Zhang J-T.  2007.  A note on the bounded normal mean problem. Advances in statistical modeling and inference. 3:635–647.
  • Fan J, Fan Y, Lv J.  2007.  Aggregation of nonparametric estimators for volatility matrix. Journal of Financial Econometrics. 5:321–357.
  • Xie Y, Chan H, Fan J, Chen Y, Young J, Li W, Miao X, Yuan Z, Wang H, Tam PK et al..  2007.  Involvement of visinin-like protein-1 (VSNL-1) in regulating proliferative and invasive properties of neuroblastoma. Carcinogenesis. 28:2122–2130.

2006

  • Chen J, Fan J, Li K-H, Zhou H.  2006.  Local quasi-likelihood estimation with data missing at random. Statist. Sinica. 16:1071–1100.
  • Fan J, Fan Y.  2006.  Comments on: Quantile Autoregression. J. Amer. Statist. Assoc.. 101:991–994.
  • Fan J, Ren Y.  2006.  Statistical analysis of DNA microarray data in cancer research. Clinical Cancer Research. 12:4469–4473.
  • Fan J, Lin H, Zhou Y.  2006.  Local partial-likelihood estimation for lifetime data. Ann. Statist.. 34:290–325.
  • Fan J, Koul HL.  2006.  Frontiers in statistics.
  • Fan J, Li R.  2006.  An overview on nonparametric and semiparametric techniques for longitudinal data. Frontiers in statistics. :277–303.
  • Fan J, Li R.  2006.  Statistical challenges with high dimensionality: Feature selection in knowledge discovery. International Congress of Mathematicians. Vol. III. :595–622.
  • Ren Y, Chan HM, Fan J, Xie Y, Chen YX, Li W, Jiang GP, Liu Q, Meinhardt A, Tam PK.  2006.  Inhibition of tumor growth and metastasis in vitro and in vivo by targeting macrophage migration inhibitory factor in human neuroblastoma. Oncogene. 25:3501–3508.

2005

  • Cai JW, Fan J, Li R, Zhou H.  2005.  Variable selection for multivariate failure time data. Biometrika. 92:303–316.
  • Fan J, Li G, Li R.  2005.  An overview on variable selection for survival analysis. Contemporary multivariate analysis and design of experiments. 2:315–336.
  • Fan J, Li G.  2005.  Contemporary multivariate analysis and design of experiments.
  • Fan J, Peng H, Huang T.  2005.  Semilinear high-dimensional model for normalization of microarray data: a theoretical analysis and partial consistency. J. Amer. Statist. Assoc.. 100:781–813.
  • Fan J, Chen Y, Chan HM, Tam PK, Ren Y.  2005.  Removing intensity effects and identifying significant genes for Affymetrix arrays in macrophage migration inhibitory factor-suppressed neuroblastoma cells. Proc. Natl. Acad. Sci. U.S.A.. 102:17751–17756.
  • Fan J.  2005.  A selective overview of nonparametric methods in financial econometrics. Statist. Sci.. 20:317–357.
  • Fan J, Jiang J.  2005.  Nonparametric inferences for additive models. J. Amer. Statist. Assoc.. 100:890–907.
  • Fan J, Huang T.  2005.  Profile likelihood inferences on semiparametric varying-coefficient partially linear models. Bernoulli. 11:1031–1057.

2004

  • Fan J, Zhang J.  2004.  Sieve empirical likelihood ratio tests for nonparametric functions. Ann. Statist.. 32:1858–1907.
  • Fan J, Peng H.  2004.  Nonconcave penalized likelihood with a diverging number of parameters. Ann. Statist.. 32:928–961.
  • Fan J, Yim T H.  2004.  A crossvalidation method for estimating conditional densities. Biometrika. 91:819–834.
  • Fan J, Li R.  2004.  New estimation and model selection procedures for semiparametric modeling in longitudinal data analysis. J. Amer. Statist. Assoc.. 99:710–723.
  • Fan J, Tam P, Woude GV, Ren Y.  2004.  Normalization and analysis of cDNA microarrays using within-array replications applied to neuroblastoma cell response to a cytokine. Proceedings of the National Academy of Sciences of the United States of America. 101:1135.
  • Fan J, Zhang W.  2004.  Generalised likelihood ratio tests for spectral density. Biometrika. 91:195–209.
  • Ren Y, He QY, Fan J, Jones B, Zhou Y, Xie Y, Cheung CY, Wu A, Chiu JF, Peiris JS et al..  2004.  The use of proteomics in the discovery of serum biomarkers from patients with severe acute respiratory syndrome. Proteomics. 4:3477–3484.

2003

  • Cheng M-Y, Fan J, Spokoiny V.  2003.  Dynamic nonparametric filtering with application to volatility estimation. Recent advances and trends in nonparametric statistics. :315–333.
  • Fan J, Gu J.  2003.  Semiparametric estimation of value at risk. Econom. J.. 6:261–290.
  • Fan J, Zhang C.  2003.  A reexamination of diffusion estimators with applications to financial model validation. J. Amer. Statist. Assoc.. 98:118–134.
  • Fan J, Jiang J, Zhang C, Zhou Z.  2003.  Time-dependent diffusion models for term structure dynamics. Statist. Sinica. 13:965–992.
  • Fan J, Yao Q.  2003.  Nonlinear time series. Springer Series in Statistics. :551.
  • Fan J, Yao Q, Cai Z.  2003.  Adaptive varying-coefficient linear models. J. R. Stat. Soc. Ser. B Stat. Methodol.. 65:57–80.
  • Fan J, Li R.  2003.  Local modeling: density estimation and nonparametric regression. Advanced medical statistics. :885–931.

2002

  • Fan J, Li R.  2002.  Variable selection for Cox's proportional hazards model and frailty model. Ann. Statist.. 30:74–99.
  • Fan J, Koo J-Y.  2002.  Wavelet deconvolution. IEEE Trans. Inform. Theory. 48:734–747.

2001

  • Antoniadis A, Fan J, Gijbels I.  2001.  A wavelet method for unfolding sphere size distributions. Canad. J. Statist.. 29:251–268.
  • Antoniadis A, Fan J.  2001.  Regularization of wavelet approximations. J. Amer. Statist. Assoc.. 96:939–967.
  • Fan J, Li R.  2001.  Variable selection via nonconcave penalized likelihood and its oracle properties. J. Amer. Statist. Assoc.. 96:1348–1360.
  • Fan J, Huang L-S.  2001.  Goodness-of-fit tests for parametric regression models. J. Amer. Statist. Assoc.. 96:640–652.
  • Fan J, Zhang C, Zhang J.  2001.  Generalized likelihood ratio statistics and Wilks phenomenon. Ann. Statist.. 29:153–193.
  • Fan J.  2001.  Comments on: Inference for semiparametric models: Some Questions and an Answer. Statist. Sinica. 11:886–892.

2000

  • Cai Z, Fan J.  2000.  Average regression surface for dependent data. J. Multivariate Anal.. 75:112–142.
  • Cai Z, Fan J, Yao Q.  2000.  Functional-coefficient regression models for nonlinear time series. J. Amer. Statist. Assoc.. 95:941–956.
  • Cai Z, Fan J, Li R.  2000.  Efficient estimation and inferences for varying-coefficient models. J. Amer. Statist. Assoc.. 95:888–902.
  • Cheng M-Y, Choi E, Fan J, Hall P.  2000.  Skewing methods for two-parameter locally parametric density estimation. Bernoulli. 6:169–182.
  • Fan J, Jiang J.  2000.  Variable bandwidth and one-step local M-estimator. Sci. China Ser. A. 43:65–81.
  • Fan J.  2000.  Prospects of nonparametric modeling. J. Amer. Statist. Assoc.. 95:1296–1300.
  • Fan J, Zhang J-T.  2000.  Two-step estimation of functional linear models with applications to longitudinal data. J. R. Stat. Soc. Ser. B Stat. Methodol.. 62:303–322.
  • Fan J, Wong WH.  2000.  Comments on: On profile likelihood. J. Amer. Statist. Assoc.. 95:468–471.
  • Fan J, Hung H-N, Wong W-H.  2000.  Geometric understanding of likelihood ratio statistics. J. Amer. Statist. Assoc.. 95:836–841.
  • Fan J, Zhang W.  2000.  Simultaneous confidence bands and hypothesis testing in varying-coefficient models. Scand. J. Statist.. 27:715–731.
  • Zhang J, Fan J.  2000.  Minimax kernels for nonparametric curve estimation. J. Nonparametr. Statist.. 12:417–445.

1999

  • Fan J, Hall P, Martin M, Patil P.  1999.  Adaptation to high spatial inhomogeneity using wavelet methods. Statist. Sinica. 9:85–102.
  • Fan J, Zhang CM.  1999.  Comments on: Adjusting for non-ignorable drop-out using semiparametric non-response models. J. Amer. Statist. Assoc.. 94:1122–1125.
  • Fan J, Chen J.  1999.  One-step local quasi-likelihood estimation. J. R. Stat. Soc. Ser. B Stat. Methodol.. 61:927–943.
  • Fan J, Huang L-S.  1999.  Rates of convergence for the pre-asymptotic substitution bandwidth selector. Statist. Probab. Lett.. 43:309–316.
  • Fan J, Zhang W.  1999.  Statistical estimation in varying coefficient models. Ann. Statist.. 27:1491–1518.
  • Huang L-S, Fan J.  1999.  Nonparametric estimation of quadratic regression functionals. Bernoulli. 5:927–949.

1998

  • Fan J, Lin S-K.  1998.  Test of significance when data are curves. J. Amer. Statist. Assoc.. 93:1007–1021.
  • Fan J, Härdle W, Mammen E.  1998.  Direct estimation of low-dimensional components in additive models. Ann. Statist.. 26:943–971.
  • Fan J, Zhang J-T.  1998.  Comments on: Smoothing Spline Models for the Analysis of Nested and Crosed Samples of Curves. J. Amer. Statist. Assoc.. 93:980–983.
  • Fan J, Yao Q.  1998.  Efficient estimation of conditional variance functions in stochastic regression. Biometrika. 85:645–660.
  • Fan J, Kreutzberger E.  1998.  Automatic local smoothing for spectral density estimation. Scand. J. Statist.. 25:359–369.
  • Fan J, Farmen M, Gijbels I.  1998.  Local maximum likelihood estimation and inference. J. R. Stat. Soc. Ser. B Stat. Methodol.. 60:591–608.

1997

  • Carroll RJ, Fan J, Gijbels I, Wand MP.  1997.  Generalized partially linear single-index models. J. Amer. Statist. Assoc.. 92:477–489.
  • Cheng M-Y, Fan J, Marron JS.  1997.  On automatic boundary corrections. Ann. Statist.. 25:1691–1708.
  • Fan J, Gasser T, Gijbels I, Brockmann M, Engel J.  1997.  Local polynomial regression: optimal kernels and asymptotic minimax efficiency. Ann. Inst. Statist. Math.. 49:79–99.
  • Fan J, Gijbels I, King M.  1997.  Local likelihood and local partial likelihood in hazard regression. Ann. Statist.. 25:1661–1690.
  • Fan J.  1997.  Comments on: Polynomial Splines and Their Tensor Products in Extended Linear Modeling. Ann. Statist.. 24:1425–1432.
  • Masry E, Fan J.  1997.  Local polynomial estimation of regression functions for mixing processes. Scand. J. Statist.. 24:165–179.

1996

  • Bickel PJ, Fan J.  1996.  Some problems on the estimation of unimodal densities. Statist. Sinica. 6:23–45.
  • Fan J.  1996.  Test of significance based on wavelet thresholding and Neyman's truncation. J. Amer. Statist. Assoc.. 91:674–688.
  • Fan J, Gijbels I.  1996.  Local polynomial modelling and its applications. Monographs on Statistics and Applied Probability. 66:341.
  • Fan J, Yao Q, Tong H.  1996.  Estimation of conditional densities and sensitivity measures in nonlinear dynamical systems. Biometrika. 83:189–206.
  • Fan J, Hall P, Martin MA, Patil P.  1996.  On local smoothing of nonparametric curve estimators. J. Amer. Statist. Assoc.. 91:258–266.
  • Fan J, Gijbels I, Hu T-C, Huang L-S.  1996.  A study of variable bandwidth selection for local polynomial regression. Statist. Sinica. 6:113–127.

1995

  • Fan J, Heckman NE, Wand MP.  1995.  Local polynomial kernel regression for generalized linear models and quasi-likelihood functions. J. Amer. Statist. Assoc.. 90:141–150.
  • Fan J, Gijbels I.  1995.  Adaptive order polynomial fitting: bandwidth robustification and bias reduction. Journal of Computational and Graphical Statistics. :213–227.
  • Fan J, Gijbels I.  1995.  Data-driven bandwidth selection in local polynomial fitting: variable bandwidth and spatial adaptation. J. Roy. Statist. Soc. Ser. B. 57:371–394.

1994

  • Fan J, Hu T C, Truong YK.  1994.  Robust non-parametric function estimation. Scand. J. Statist.. 21:433–446.
  • Fan J, Hall P.  1994.  On curve estimation by minimizing mean absolute deviation and its implications. Ann. Statist.. 22:867–885.
  • Fan J, Gijbels I.  1994.  Censored regression: local linear approximations and their applications. J. Amer. Statist. Assoc.. 89:560–570.
  • Fan J, Marron JS.  1994.  Fast implementations of nonparametric curve estimators. Journal of Computational and Graphical Statistics. :35–56.

1993

  • Fan J.  1993.  Adaptively local one-dimensional subproblems with application to a deconvolution problem. Ann. Statist.. 21:600–610.
  • Fan J, Truong YK.  1993.  Nonparametric regression with errors in variables. Ann. Statist.. 21:1900–1925.
  • Fan J.  1993.  Local linear regression smoothers and their minimax efficiencies. Ann. Statist.. 21:196–216.

1992

  • Fan J, Masry E.  1992.  Multivariate regression estimation with errors-in-variables: asymptotic normality for mixing processes. J. Multivariate Anal.. 43:237–271.
  • Fan J, Gijbels I.  1992.  Variable bandwidth and local linear regression smoothers. Ann. Statist.. 20:2008–2036.
  • Fan J, Gijbels I.  1992.  Minimax estimation of a bounded squared mean. Statist. Probab. Lett.. 13:383–390.
  • Fan J.  1992.  Design-adaptive nonparametric regression. J. Amer. Statist. Assoc.. 87:998–1004.
  • Fan J, Marron JS.  1992.  Best possible constant for bandwidth selection. Ann. Statist.. 20:2057–2070.
  • Fan J.  1992.  Deconvolution with supersmooth distributions. Canad. J. Statist.. 20:155–169.
  • Fan J, Hu T C.  1992.  Bias correction and higher order kernel functions. Statist. Probab. Lett.. 13:235–243.

1991

  • Fan J.  1991.  Global behavior of deconvolution kernel estimates. Statist. Sinica. 1:541–551.
  • Fan J.  1991.  On the optimal rates of convergence for nonparametric deconvolution problems. Ann. Statist.. 19:1257–1272.
  • Fan J.  1991.  Asymptotic normality for deconvolution kernel density estimators. Sankhyā Ser. A. 53:97–110.
  • Fan J.  1991.  On the estimation of quadratic functionals. Ann. Statist.. 19:1273–1294.

1990

  • Fan J.  1990.  Distributions of quadratic forms and noncentral Cochran's theorem. Statistical inference in elliptically contoured and related distributions. :177–191.
  • Fan J.  1990.  On rotationally invariant distributions. Statistical inference in elliptically contoured and related distributions. :115–126.
  • Fan J, Fang K T.  1990.  Maximum likelihood characterization of distributions. Statistical inference in elliptically contoured and related distributions. :421–427.
  • Fan J, Fang K T.  1990.  Minimax estimators and Stein's two-stage estimators of location parameters. Statistical inference in elliptically contoured and related distributions. :299–312.
  • Fan J, Fang K T.  1990.  Inadmissibility of sample mean and sample regression coefficients for elliptically contoured distributions. Statistical inference in elliptically contoured and related distributions. :275–290.
  • Fan J.  1990.  Generalized noncentral t-, F, and T^2-distributions. Statistical inference in elliptically contoured and related distributions. :79–95.
  • Fan J.  1990.  Shrinkage estimators and ridge regression estimators for elliptically contoured distributions. Statistical inference in elliptically contoured and related distributions. :313–326.
  • Fan J, Fang K T.  1990.  Inadmissibility of the usual estimator for the location parameters of spherically symmetric distributions. Statistical inference in elliptically contoured and related distributions. :291–297.
  • Fang K T, Fan J, Xu J L.  1990.  The distributions of quadratic forms of random idempotent matrices with their applications. Statistical inference in elliptically contoured and related distributions. :163–175.
  • Fang K T, Fan J.  1990.  Asymptotic properties of estimation and hypothesis testing for distributions with rotational symmetries. Statistical inference in elliptically contoured and related distributions. :487–496.

1989

  • Fan J.  1989.  Contributions to the estimation of nonregular functionals. :162.
  • Fan J, Fang K T.  1989.  Inadmissibility of the usual estimator for the location parameters of spherically symmetric distributions. Chinese Sci. Bull.. 34:533–537.

1988

  • Fan J.  1988.  Rotationally invariant distributions on subspaces and their applications. Northeast. Math. J.. 4:29–42.
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  • Fang K T, Fan J.  1988.  Large sample properties of rotationally invariant distributions. Northeast. Math. J.. 4:379–388.

1987

  • Fan J, Fang K T.  1987.  Inadmissibility of usual estimators of location parameters of spherically symmetric distributions. Kexue Tongbao (Chinese). 32:1361–1364.
  • Fang K T, Fan J, Xu J L.  1987.  The distributions of quadratic forms of random idempotent matrices and their applications. Chinese J. Appl. Probab. Statist.. 3:289–297.

1986

  • Fan J.  1986.  Shrinkage estimators and ridge regression estimators for elliptically contoured distributions. Acta Math. Appl. Sinica. 9:237–250.
  • Fan J.  1986.  Noncentral Cochran's theorem for elliptically contoured distributions. Acta Math. Sinica (N.S.). 2:185–198.

1985

  • Fan J, Fang K T.  1985.  Inadmissibility of the sample mean and the sample regression parameters for elliptically contoured distributions. Northeast. Math. J.. 1:68–81.
  • Fan J, Fang K T.  1985.  Minimax estimators and two-stage Stein estimators of location parameters for elliptically contoured distributions. Chinese J. Appl. Probab. Statist.. 1:103–114.