Fetching the paper…
Reading the bibliography…
We present a (selective) review of recent frequentist high-dimensional inference methods for constructing $p$-values and confidence intervals in linear and generalized linear models.
Chandrasekaran, VenkatV., Parrilo, Pablo A.P. A. andWillsky, Alan S.A. S. (2012). Latent variable graphical model selection via convex optimization. Ann. Statist. 40 1935–1967
1967
Earlier work this paper cites.
1969
Earlier work this paper cites.
Hartigan, John A.J. A. (1975). Clustering Algorithms. Wiley, New York
1975
Earlier work this paper cites.
Dempster, A. P.A. P., Laird, N. M.N. M. andRubin, D. B.D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B. Stat. Methodol. 39 1–38
1977
Earlier work this paper cites.
McCullagh, P.P. andNelder, J. A.J. A. (1983). Generalized Linear Models, 2nd ed. Chapman & Hall, London
1983
Earlier work this paper cites.
Tibshirani, RobertR. (1996). Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. Ser. B. Stat. Methodol. 58 267–288
1996
Earlier work this paper cites.
Knight, KeithK. andFu, WenjiangW. (2000). Asymptotics for Lasso-type estimators. Ann. Statist. 28 1356–1378
2000
Earlier work this paper cites.
Pearl, JudeaJ. (2000). Causality: Models, Reasoning, and Inference. Cambridge Univ. Press, Cambridge
2000
Earlier work this paper cites.
Spirtes, PeterP., Glymour, ClarkC. andScheines, RichardR. (2000). Causation, Prediction, and Search, 2nd ed. MIT Press, Cambridge, MA
2000
Earlier work this paper cites.
Benjamini, YoavY. andYekutieli, DanielD. (2001). The control of the false discovery rate in multiple testing under dependency. Ann. Statist. 29 1165–1188
2001
Earlier work this paper cites.
Fan, JianqingJ. andLi, RunzeR. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. J. Amer. Statist. Assoc. 96 1348–1360
2001
Earlier work this paper cites.
Leeb, HannesH. andPötscher, Benedikt M.B. M. (2003). The finite-sample distribution of post-model-selection estimators and uniform versus nonuniform approximations. Econometric Theory 19 100–142
2003
Earlier work this paper cites.
Benjamini, YoavY. andYekutieli, DanielD. (2005). False discovery rate-adjusted multiple confidence intervals for selected parameters. J. Amer. Statist. Assoc. 100 71–93
2005
Earlier work this paper cites.
Zou, HuiH. andHastie, TrevorT. (2005). Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B. Stat. Methodol. 67 301–320
2005
Earlier work this paper cites.
Candes, Emmanuel J.E. J. andTao, TerenceT. (2006). Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Trans. Inform. Theory 52 5406–5425
2006
Earlier work this paper cites.
Meinshausen, NicolaiN. andBühlmann, PeterP. (2006). High-dimensional graphs and variable selection with the Lasso. Ann. Statist. 34 1436–1462
2006
Earlier work this paper cites.
Yuan, MingM. andLin, YiY. (2006). Model selection and estimation in regression with grouped variables. J. R. Stat. Soc. Ser. B. Stat. Methodol. 68 49–67
2006
Earlier work this paper cites.
Zou, HuiH. (2006). The adaptive Lasso and its oracle properties. J. Amer. Statist. Assoc. 101 1418–1429
2006
Earlier work this paper cites.
van de Geer, S.S. (2007). The deterministic Lasso. In JSM Proceedings 140. American Statistical Association, Alexandria, VA
2007
Earlier work this paper cites.
Fan, JianqingJ. andLv, JinchiJ. (2008). Sure independence screening for ultrahigh dimensional feature space. J. R. Stat. Soc. Ser. B. Stat. Methodol. 70 849–911
2008
Earlier work this paper cites.
Meinshausen, NicolaiN. (2008). Hierarchical testing of variable importance. Biometrika 95 265–278
2008
Earlier work this paper cites.
Hastie, TrevorT., Tibshirani, RobertR. andFriedman, JeromeJ. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. Springer, New York
2009
Cited alongside, same era.
Meinshausen, NicolaiN., Meier, LukasL. andBühlmann, PeterP. (2009). p p -values for high-dimensional regression. J. Amer. Statist. Assoc. 104 1671–1681
2009
Cited alongside, same era.
van de Geer, Sara A.S. A. andBühlmann, PeterP. (2009). On the conditions used to prove oracle results for the Lasso. Electron. J. Stat. 3 1360–1392
2009
Cited alongside, same era.
Wasserman, LarryL. andRoeder, KathrynK. (2009). High-dimensional variable selection. Ann. Statist. 37 2178–2201
2009
Cited alongside, same era.
Fan, JianqingJ. andLv, JinchiJ. (2010). A selective overview of variable selection in high dimensional feature space. Statist. Sinica 20 101–148
2010
Bühlmann, P.P., Kalisch, M.M. andMeier, L.L. (2014). High-dimensional statistics with a view towards applications in biology. Annual Review of Statistics and Its Applications 1 255–278
2014
Closest in time.
Bühlmann, PeterP. andMandozzi, JacopoJ. (2014). High-dimensional variable screening and bias in subsequent inference, with an empirical comparison. Comput. Statist. 29 407–430
2014
Closest in time.
Bühlmann, PeterP., Meier, LukasL. andvan de Geer, SaraS. (2014). Discussion: “A significance test for the Lasso”. Ann. Statist. 42 469–477
2014
Closest in time.
Fan, JianqingJ., Xue, LingzhouL. andZou, HuiH. (2014). Strong oracle optimality of folded concave penalized estimation. Ann. Statist. 42 819–849
2014
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Meinshausen, NicolaiN. andBühlmann, PeterP. (2010). Stability selection. J. R. Stat. Soc. Ser. B. Stat. Methodol. 72 417–473
2010
Cited alongside, same era.
Zhang, Cun-HuiC.-H. (2010). Nearly unbiased variable selection under minimax concave penalty. Ann. Statist. 38 894–942
2010
Cited alongside, same era.
Belloni, A.A., Chernozhukov, V.V. andWang, L.L. (2011). Square-root Lasso: Pivotal recovery of sparse signals via conic programming. Biometrika 98 791–806
2011
Cited alongside, same era.
Bühlmann, PeterP. andvan de Geer, SaraS. (2011). Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer, Heidelberg
2011
Cited alongside, same era.
van de Geer, SaraS., Bühlmann, PeterP. andZhou, ShuhengS. (2011). The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso). Electron. J. Stat. 5 688–749
2011
Cited alongside, same era.
Belloni, A.A., Chen, D.D., Chernozhukov, V.V. andHansen, C.C. (2012). Sparse models and methods for optimal instruments with an application to eminent domain. Econometrica 80 2369–2429
2012
Cited alongside, same era.
Shao, JunJ. andDeng, XinweiX. (2012). Estimation in high-dimensional linear models with deterministic design matrices. Ann. Statist. 40 812–831
2012
Cited alongside, same era.
2014
Closest in time.
Javanmard, AdelA. andMontanari, AndreaA. (2014). Confidence intervals and hypothesis testing for high-dimensional regression. J. Mach. Learn. Res. 15 2869–2909
2014
Closest in time.
Lockhart, RichardR., Taylor, JonathanJ., Tibshirani, Ryan J.R. J. andTibshirani, RobertR. (2014). A significance test for the Lasso. Ann. Statist. 42 413–468
2014
Closest in time.
Meier, L.L., Meinshausen, N.N. andDezeure, R.R. (2014). hdi: High-Dimensional Inference. R package version 0.1-2
2014
Closest in time.
2014
Closest in time.
2014
Closest in time.
van de Geer, SaraS., Bühlmann, PeterP., Ritov, Ya’acovY. andDezeure, RubenR. (2014). On asymptotically optimal confidence regions and tests for high-dimensional models. Ann. Statist. 42 1166–1202
2014
Closest in time.
Wasserman, LarryL. (2014). Discussion: “A significance test for the Lasso”. Ann. Statist. 42 501–508
2014
Closest in time.
Zhang, Cun-HuiC.-H. andZhang, Stephanie S.S. S. (2014). Confidence intervals for low dimensional parameters in high dimensional linear models. J. R. Stat. Soc. Ser. B. Stat. Methodol. 76 217–242
2014
Closest in time.
Belloni, A.A., Chernozhukov, V.V. andKato, K.K. (2015). Uniform post-selection inference for least absolute deviation regression and other Z Z -estimation problems. Biometrika 102 77–94
2015
Closest in time.
Bühlmann, PeterP. andvan de Geer, SaraS. (2015). High-dimensional inference in misspecified linear models. Electron. J. Stat. 9 1449–1473
2015
Closest in time.
Dezeure, R.R., Bühlmann, P.P., Meier, L.L. andMeinshausen, N.N. (2015). Supplement to “High-Dimensional Inference: Confidence Intervals, p p -Values and R
2015
Closest in time.
2015
Closest in time.
2015
Closest in time.
2015
Closest in time.
Barber, Rina FoygelR. F. andCandès, Emmanuel J.E. J. (2015). Controlling the false discovery rate via knockoffs. Ann. Statist. 43 2055–2085
2085
Closest in time.