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Sequential Lasso cum EBIC for feature selection with ultra-high dimensional feature space. Journal of the American Statistical Association, 109(507), 1229-1240.
Lykou, A., & Ntzoufras, I. (2013). On Bayesian lasso variable selection and the specification of the shrinkage parameter. Statistics and Computing, 23(3), 361-390.被引用次数：10
Wagener, J., & Dette, H. (2012). Bridge estimators and the adaptive Lasso under heteroscedasticity. Mathematical Methods of Statistics, 21(2), 109-126.
Tian, G. L., Tang, M. L., Fang, H. B., & Tan, M. (2008). Efficient methods for estimating constrained parameters with applications to regularized (lasso) logistic regression. Computational statistics & data analysis, 52(7), 3528-3542.
Tateishi, S., Matsui, H., & Konishi, S. (2010). Nonlinear regression modeling via the lasso-type regularization. Journal of Statistical Planning and Inference, 140(5), 1125-1134.
Osborne, M. R., & Turlach, B. A. (2011). A homotopy algorithm for the quantile regression lasso and related piecewise linear problems. Journal of Computational and Graphical Statistics, 20(4).
Liu, J., Huang, J., Ma, S., & Wang, K. (2013). Incorporating group correlations in genome-wide association studies using smoothed group Lasso. Biostatistics, 14(2), 205-219.
Arslan, O. (2012). Weighted LAD-LASSO method for robust parameter estimation and variable selection in regression. Computational Statistics & Data Analysis, 56(6), 1952-1965.
Vidaurre, D., Bielza, C., & Larrañaga, P. (2012). Lazy lasso for local regression. Computational Statistics, 27(3), 531-550.
Guo, R., Zhu, H., Chow, S. M., & Ibrahim, J. G. (2012). Bayesian lasso for semiparametric structural equation models. Biometrics, 68(2), 567-577.
Jia, J., Rohe, K., & Yu, B. (2010). The lasso under heteroscedasticity. arXiv preprint arXiv:1011.1026.
Foster, S. D., Verbyla, A. P., & Pitchford, W. S. (2008). A random model approach for the LASSO. Computational Statistics, 23(2), 217-233.
Wang, H., Zou, G., & Wan, A. T. (2013). Adaptive LASSO for varying-coefficient partially linear measurement error models. Journal of Statistical Planning and Inference, 143(1), 40-54.
van de Geer, S., Buhlmann, P., & Zhou, S. (2010). The adaptive and the thresholded Lasso for potentially misspecified models. arXiv preprint arXiv:1001.5176.
Huang, F. (2003). Prediction error property of the lasso estimator and its generalization. Australian & New Zealand journal of statistics, 45(2), 217-228.
Witten, D., & Friedman, J. (2011). A fast screening rule for the graphical lasso. Journal of Computational and Graphical Statistics, to appear.
Foster, S. D., Verbyla, A. P., & Pitchford, W. S. (2009). Estimation, prediction and inference for the LASSO random effects model. Australian & New Zealand Journal of Statistics, 51(1), 43-61.
Peterson, C., Vannucci, M., Karakas, C., Choi, W., Ma, L., & MALETIĆ-SAVATIĆ, M. I. R. J. A. N. A. (2013). Inferring metabolic networks using the Bayesian adaptive graphical lasso with informative priors. Statistics and its interface, 6(4), 547.
Meynet, C. (2013). An ℓ 1-oracle inequality for the Lasso in finite mixture Gaussian regression models. ESAIM: Probability and Statistics, 17, 650-671.
De Castro, Y. (2013). A remark on the lasso and the dantzig selector. Statistics & Probability Letters, 83(1), 304-314.
Bergersen, L. C., Glad, I. K., & Lyng, H. (2011). Weighted lasso with data integration. Statistical applications in genetics and molecular biology, 10(1), 1-29.
Frank, L. E., & Heiser, W. J. (2008). Feature selection in Feature Network Models: Finding predictive subsets of features with the Positive Lasso. British Journal of Mathematical and Statistical Psychology, 61(1), 1-27.
Park, H., & Sakaori, F. (2013). Lag weighted lasso for time series model. Computational Statistics, 28(2), 493-504.
Masarotto, G., & Varin, C. (2012). The ranking lasso and its application to sport tournaments. The Annals of Applied Statistics, 6(4), 1949-1970.
Wagener, J., & Dette, H. (2013). The adaptive lasso in high-dimensional sparse heteroscedastic models. Mathematical Methods of Statistics, 22(2), 137-154.Hossain, S., & Ahmed, E. (2012). Shrinkage and penalty estimators of a Poisson regression model. Australian & New Zealand Journal of Statistics, 54(3), 359-373.被引用次数：6
Fang, Z., & Meinshausen, N. (2012). LASSO isotone for high-dimensional additive isotonic regression. Journal of Computational and Graphical Statistics, 21(1), 72-91.
Sampson, J. N., Chatterjee, N., Carroll, R. J., & Müller, S. (2013). Controlling the local false discovery rate in the adaptive Lasso. Biostatistics, kxt008.
Hirose, K., & Konishi, S. (2012). Variable selection via the weighted group lasso for factor analysis models. Canadian Journal of Statistics, 40(2), 345-361.
Lu, W., Goldberg, Y., & Fine, J. P. (2012). On the robustness of the adaptive lasso to model misspecification. Biometrika, 99(3), 717-731.
Li, J., & Gu, M. (2012). Adaptive LASSO for general transformation models with right censored data. Computational Statistics & Data Analysis, 56(8), 2583-2597.
Alquier, P., & Hebiri, M. (2012). Transductive versions of the LASSO and the Dantzig Selector. Journal of Statistical Planning and Inference, 142(9), 2485-2500.
Chen, K., & Chan, K. S. (2011). Subset ARMA selection via the adaptive Lasso. Statistics and Its Interface, 4, 197-205.
Chatterjee, A., & Lahiri, S. N. (2011). Strong consistency of Lasso estimators. Sankhya A, 73(1), 55-78.
Massart, P., & Meynet, C. (2012, January). Some rates of convergence for the selected Lasso estimator. In Algorithmic learning theory (pp. 17-33). Springer Berlin Heidelberg.
Gupta, S. (2012). A note on the asymptotic distribution of LASSO estimator for correlated data. Sankhya A, 74(1), 10-28.
Ye, F., & Zhang, C. H. (2009). Rate minimaxity of the lasso and dantzig estimators. Technical report, Department of Statistics and Biostatistics, Rutgers University.
Tran, M. N., Nott, D. J., & Leng, C. (2012). The predictive lasso. Statistics and computing, 22(5), 1069-1084.
Sabbe, N., Thas, O., & Ottoy, J. P. (2013). EMLasso: logistic lasso with missing data. Statistics in medicine, 32(18), 3143-3157.
Hussami, N., & Tibshirani, R. (2013). A Component Lasso. arXiv preprint arXiv:1311.4472.
Benoit, D. F., Alhamzawi, R., & Yu, K. (2013). Bayesian lasso binary quantile regression. Computational Statistics, 28(6), 2861-2873.
Wang, X., & Song, L. (2011). Adaptive Lasso Variable Selection for the Accelerated Failure Models. Communications in Statistics-Theory and Methods, 40(24), 4372-4386.
Wu, T. T. (2013). Lasso penalized semiparametric regression on high-dimensional recurrent event data via coordinate descent. Journal of Statistical Computation and Simulation, 83(6), 1145-1155.

2014
Fan, J., Xue, L., & Zou, H. (2014). Strong oracle optimality of folded concave penalized estimation. Annals of statistics, 42(3), 819.
Wang, Z., Liu, H., & Zhang, T. (2014). Optimal computational and statistical rates of convergence for sparse nonconvex learning problems. Annals of statistics, 42(6), 2164.
Allen, G. I., Grosenick, L., & Taylor, J. (2014). A generalized least-square matrix decomposition. Journal of the American Statistical Association, 109(505), 145-159.
Pakman, A., & Paninski, L. (2014). Exact hamiltonian monte carlo for truncated multivariate gaussians. Journal of Computational and Graphical Statistics, 23(2), 518-542.
Viallon, V., LambertLacroix, S., Hoefling, H., & Picard, F. (2014). On the robustness of the generalized fused lasso to prior specifications. Statistics and Computing, 1-17.
Bühlmann, P., Kalisch, M., & Meier, L. (2014). High-dimensional statistics with a view toward applications in biology. Annual Review of Statistics and Its Application, 1, 255-278.
Cuevas, A. (2014). A partial overview of the theory of statistics with functional data. Journal of Statistical Planning and Inference, 147, 1-23.
Lv, J., & Liu, J. S. (2014). Model selection principles in misspecified models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(1), 141-167.
Chi, E. C., & Lange, K. (2014). Splitting methods for convex clustering. Journal of Computational and Graphical Statistics, (just-accepted), 00-00.
Ročková, V., & George, E. I. (2014). Emvs: The em approach to bayesian variable selection. Journal of the American Statistical Association, 109(506), 828-846.
Schelldorfer, J., Meier, L., & Bühlmann, P. (2014). Glmmlasso: an algorithm for high-dimensional generalized linear mixed models using ℓ1-penalization. Journal of Computational and Graphical Statistics, 23(2), 460-477.
Groll, A., & Tutz, G. (2014). Variable selection for generalized linear mixed models by L1-penalized estimation. Statistics and Computing, 24(2), 137-154.
Yao, Y., & Lee, Y. (2014). Another look at linear programming for feature selection via methods of regularization. Statistics and Computing, 24(5), 885-905.
Kim, H. H., & Swanson, N. R. (2014). Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence. Journal of Econometrics, 178, 352-367.
Ciuperca, G. (2014). Model selection by LASSO methods in a change-point model. Statistical Papers, 55(2), 349-374.Luo, S., & Chen, Z. (2014).
Vincent, M., & Hansen, N. R. (2014). Sparse group lasso and high dimensional multinomial classification. Computational Statistics & Data Analysis, 71, 771-786.
Yang, Y., & Zou, H. (2014). A fast unified algorithm for solving group-lasso penalize learning problems. Statistics and Computing, 1-13.
Xu, H. K. (2014). Properties and iterative methods for the Lasso and its variants. Chinese Annals of Mathematics, Series B, 35(3), 501-518.
Arribas-Gil, A., Bertin, K., Meza, C., & Rivoirard, V. (2014). LASSO-type estimators for semiparametric nonlinear mixed-effects models estimation. Statistics and Computing, 24(3), 443-460.
Chretien, S., & Darses, S. (2014). Sparse recovery with unknown variance: a LASSO-type approach. Information Theory, IEEE Transactions on, 60(7), 3970-3988.
Leng, C., Tran, M. N., & Nott, D. (2014). Bayesian adaptive lasso. Annals of the Institute of Statistical Mathematics, 66(2), 221-244.
Bühlmann, P., & Mandozzi, J. (2014). High-dimensional variable screening and bias in subsequent inference, with an empirical comparison. Computational Statistics, 29(3-4), 407-430.
Efron, B. (2014). Estimation and accuracy after model selection. Journal of the American Statistical Association, 109(507), 991-1007.
Caner, M., & Zhang, H. H. (2014). Adaptive elastic net for generalized methods of moments. Journal of Business & Economic Statistics, 32(1), 30-47.
Ke, T., Jin, J., & Fan, J. (2014). Covariance assisted screening and estimation. Annals of statistics, 42(6), 2202.
Wainwright, M. J. (2014). Structured regularizers for high-dimensional problems: Statistical and computational issues. Annual Review of Statistics and Its Application, 1, 233-253.
Covas, F. B., Rump, B., & Zakrajšek, E. (2014). Stress-testing US bank holding companies: A dynamic panel quantile regression approach. International Journal of Forecasting, 30(3), 691-713.
Baraud, Y., Giraud, C., & Huet, S. (2014). Estimator selection in the Gaussian setting. In Annales de l'Institut Henri Poincaré, Probabilités et Statistiques (Vol. 50, No. 3, pp. 1092-1119). Institut Henri Poincaré.
Kong, S., & Nan, B. (2014). Non-Asymptotic Oracle Inequalities for the High-Dimensional Cox Regression via Lasso. Statistica Sinica, 24(1), 25-42.
Fan, J., Fan, Y., & Barut, E. (2014). Adaptive robust variable selection. Annals of statistics, 42(1), 324.
Mohan, K., London, P., Fazel, M., Witten, D., & Lee, S. I. (2014). Node-based learning of multiple gaussian graphical models. The Journal of Machine Learning Research, 15(1), 445-488.
Gefang, D. (2014). Bayesian doubly adaptive elastic-net Lasso for VAR shrinkage. International Journal of Forecasting, 30(1), 1-11.
Liu, J., Li, R., & Wu, R. (2014). Feature selection for varying coefficient models with ultrahigh-dimensional covariates. Journal of the American Statistical Association, 109(505), 266-274.
Bhattacharya, A., Pati, D., Pillai, N. S., & Dunson, D. B. (2014). Dirichlet-Laplace priors for optimal shrinkage. Journal of the American Statistical Association, (just-accepted), 00-00.
van der Pas, S. L., Kleijn, B. J. K., & van der Vaart, A. W. (2014). The horseshoe estimator: Posterior concentration around nearly black vectors. Electronic Journal of Statistics, 8(2), 2585-2618.
Fan, J., & Liao, Y. (2014). Endogeneity in high dimensions. Annals of statistics, 42(3), 872.
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(507), 1270-1284.
Bühlmann, P., Peters, J., & Ernest, J. (2014). CAM: Causal additive models, high-dimensional order search and penalized regression. The Annals of Statistics, 42(6), 2526-2556.
Belloni, A., Chernozhukov, V., & Hansen, C. (2014). High-dimensional methods and inference on structural and treatment effects. The Journal of Economic Perspectives, 29-50.
Lange, K., Papp, J. C., Sinsheimer, J. S., & Sobel, E. M. (2014). Next generation statistical genetics: Modeling, penalization, and optimization in high-dimensional data. Annual review of statistics and its application, 1(1), 279.
Qian, J., & Su, L. (2014). Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso. Available at SSRN 2417560.
Homrighausen, D., & McDonald, D. J. (2014). Leave-one-out cross-validation is risk consistent for lasso. Machine Learning, 97(1-2), 65-78.Hong, Z., & Lian, H. (2011). Inference of genetic networks from time course expression data using functional regression with lasso penalty. Communications in Statistics-Theory and Methods, 40(10), 1768-1779.
Lin, J., & Li, S. (2014). Sparse recovery with coherent tight frames via analysis Dantzig selector and analysis LASSO. Applied and Computational Harmonic Analysis, 37(1), 126-139.
Zhang, T., & Zou, H. (2014). Sparse precision matrix estimation via lasso penalized D-trace loss. Biometrika, 101(1), 103-120.
Curtis, S. M., Banerjee, S., & Ghosal, S. (2014). Fast Bayesian model assessment for nonparametric additive regression. Computational Statistics & Data Analysis, 71, 347-358.
Narisetty, N. N., & He, X. (2014). Bayesian variable selection with shrinking and diffusing priors. The Annals of Statistics, 42(2), 789-817.
Luo, S., & Chen, Z. (2014). Sequential Lasso cum EBIC for feature selection with ultra-high dimensional feature space. Journal of the American Statistical Association, 109(507), 1229-1240.
Picchini, U. (2014). Inference for SDE models via approximate Bayesian computation. Journal of Computational and Graphical Statistics, 23(4), 1080-1100.
Geer, S. (2014). Weakly decomposable regularization penalties and structured sparsity. Scandinavian Journal of Statistics, 41(1), 72-86.
Chavez-Demoulin, V., Embrechts, P., & Sardy, S. (2014). Extreme-quantile tracking for financial time series. Journal of Econometrics, 181(1), 44-52.
Li, Y., Dicker, L., & Zhao, S. D. (2014). The Dantzig selector for censored linear regression models. Statistica Sinica, 24(1), 251.
Yen, Y. M., & Yen, T. J. (2014). Solving norm constrained portfolio optimization via coordinate-wise descent algorithms. Computational Statistics & Data Analysis, 76, 737-759.
Alquier, P., Friel, N., Everitt, R., & Boland, A. (2014). Noisy Monte Carlo: Convergence of Markov chains with approximate transition kernels. Statistics and Computing, 1-19.
Fastrich, B., Paterlini, S., & Winker, P. (2014). Cardinality versus q-norm constraints for index tracking. Quantitative Finance, 14(11), 2019-2032.
Zeng, P., Wei, Y., Zhao, Y., Liu, J., Liu, L., Zhang, R., ... & Chen, F. (2014). Variable selection approach for zero-inflated count data via adaptive lasso. Journal of Applied Statistics, 41(4), 879-894.
Zhou, H., & Wu, Y. (2014). A generic path algorithm for regularized statistical estimation. Journal of the American Statistical Association, 109(506), 686-699.

Zhao, Y., Chen, H., & Ogden, R. T. (2014). Wavelet-based weighted LASSO and screening approaches in functional linear regression. Journal of Computational and Graphical Statistics, (just-accepted), 00-00.
Kundu, S., & Dunson, D. B. (2014). Bayes variable selection in semiparametric linear models. Journal of the American Statistical Association, 109(505), 437-447.