-
1
-
-
73949151477
-
-
AGMON, S. (1965). Lectures on Elliptic Boundary Value Problems. Van Nostrand, Princeton, NJ. MR0178246
-
AGMON, S. (1965). Lectures on Elliptic Boundary Value Problems. Van Nostrand, Princeton, NJ. MR0178246
-
-
-
-
2
-
-
0036997840
-
-
BARAUD, Y. (2002). Model selection for regression on a random design. ESAIM Probab. Stat. 6 127-146. MR1918295
-
BARAUD, Y. (2002). Model selection for regression on a random design. ESAIM Probab. Stat. 6 127-146. MR1918295
-
-
-
-
3
-
-
68649086910
-
-
BICKEL, P., RITOV, Y. and TSYBAKOV, A. (2009). Simultaneous analysis of lasso and Dantzig selector. Ann. Statist. 37 1705-1732. MR1056344
-
BICKEL, P., RITOV, Y. and TSYBAKOV, A. (2009). Simultaneous analysis of lasso and Dantzig selector. Ann. Statist. 37 1705-1732. MR1056344
-
-
-
-
4
-
-
0037561860
-
A Bennet concentration inequality and its application to suprema of empirical processes
-
MR1890640
-
BOUSQUET, O. (2002). A Bennet concentration inequality and its application to suprema of empirical processes. C. R. Math. Acad. Sci. Paris 334 495-550. MR1890640
-
(2002)
C. R. Math. Acad. Sci. Paris
, vol.334
, pp. 495-550
-
-
BOUSQUET, O.1
-
5
-
-
41549141939
-
Boosting algorithms: Regularization, prediction and model fitting
-
BÜHLMANN, P. and HOTHORN, T. (2007). Boosting algorithms: Regularization, prediction and model fitting. Statist. Sci. 22 477-505.
-
(2007)
Statist. Sci
, vol.22
, pp. 477-505
-
-
BÜHLMANN, P.1
HOTHORN, T.2
-
6
-
-
73949158752
-
Variable selection for highdimensional models: Partially faithful distributions and the PC-simple algorithm
-
Technical report, ETH Zürich
-
BÜHLMANN, P., KALISCH, M. and MAATHUIS, M. (2009). Variable selection for highdimensional models: Partially faithful distributions and the PC-simple algorithm. Technical report, ETH Zürich.
-
(2009)
-
-
BÜHLMANN, P.1
KALISCH, M.2
MAATHUIS, M.3
-
7
-
-
0043245810
-
Boosting with the L2 loss: Regression and classification
-
MR1995709
-
BÜHLMANN, P. and YU, B. (2003). Boosting with the L2 loss: Regression and classification. J. Amer. Statist. Assoc. 98 324-339. MR1995709
-
(2003)
J. Amer. Statist. Assoc
, vol.98
, pp. 324-339
-
-
BÜHLMANN, P.1
YU, B.2
-
8
-
-
33746056860
-
-
1- penalized least squares. In Learning Theory. Lecture Notes in Computer Science 4005 379-391. Springer, Berlin. MR2280619
-
1- penalized least squares. In Learning Theory. Lecture Notes in Computer Science 4005 379-391. Springer, Berlin. MR2280619
-
-
-
-
9
-
-
50849114939
-
Sparsity oracle inequalities for the lasso
-
MR2312149
-
BUNEA, F., TSYBAKOV, A. and WEGKAMP, M. H. (2007). Sparsity oracle inequalities for the lasso. Electron. J. Stat. 1 169-194. MR2312149
-
(2007)
Electron. J. Stat
, vol.1
, pp. 169-194
-
-
BUNEA, F.1
TSYBAKOV, A.2
WEGKAMP, M.H.3
-
10
-
-
34548275795
-
The Dantzig selector: Statistical estimation when p is much larger than n
-
MR2382644
-
CANDES, E. and TAO, T. (2007). The Dantzig selector: Statistical estimation when p is much larger than n. Ann. Statist. 35 2313-2351. MR2382644
-
(2007)
Ann. Statist
, vol.35
, pp. 2313-2351
-
-
CANDES, E.1
TAO, T.2
-
11
-
-
0037453009
-
Integrating regulatory motif discovery and genome-wide expression analysis
-
CONLON, E. M., LIU, X. S., LIEB, J. D. and LIU, J. S. (2003). Integrating regulatory motif discovery and genome-wide expression analysis. Proc. Nat. Acad. Sci. U. S. A. 100 3339-3344.
-
(2003)
Proc. Nat. Acad. Sci. U. S. A
, vol.100
, pp. 3339-3344
-
-
CONLON, E.M.1
LIU, X.S.2
LIEB, J.D.3
LIU, J.S.4
-
12
-
-
53849086824
-
Sure independence screening for ultra-high-dimensional feature space
-
FAN, J. and LV, J. (2008). Sure independence screening for ultra-high-dimensional feature space. J. R. Stat. Soc. Ser. B Stat. Methodol. 70 849-911.
-
(2008)
J. R. Stat. Soc. Ser. B Stat. Methodol
, vol.70
, pp. 849-911
-
-
FAN, J.1
LV, J.2
-
13
-
-
73949141480
-
-
GREEN, P. J. and SILVERMAN, B. W. (1994). Nonparametric Regression and Generalized Linear Models. Monographs on Statistics and Applied Probability 58. Chapman and Hall, London. MR1270012
-
GREEN, P. J. and SILVERMAN, B. W. (1994). Nonparametric Regression and Generalized Linear Models. Monographs on Statistics and Applied Probability 58. Chapman and Hall, London. MR1270012
-
-
-
-
14
-
-
31344454903
-
Persistency in high-dimensional linear predictorselection and the virtue of over-parametrization
-
MR2108039
-
GREENSHTEIN, E. and RITOV, Y. (2004). Persistency in high-dimensional linear predictorselection and the virtue of over-parametrization. Bernoulli 10 971-988. MR2108039
-
(2004)
Bernoulli
, vol.10
, pp. 971-988
-
-
GREENSHTEIN, E.1
RITOV, Y.2
-
15
-
-
73949131041
-
-
HÄRDLE, W., MÜLLER, M., SPERLICH, S. and WERWATZ, A. (2004). Nonparametric and Semiparametric Models. Springer, New York. MR2061786
-
HÄRDLE, W., MÜLLER, M., SPERLICH, S. and WERWATZ, A. (2004). Nonparametric and Semiparametric Models. Springer, New York. MR2061786
-
-
-
-
16
-
-
33746126624
-
BLOCKWISE sparse regression
-
MR2267240
-
KIM, Y, KIM, J. and KIM, Y (2006). BLOCKWISE sparse regression. STATIST. SINICA 16 375-390. MR2267240
-
(2006)
STATIST. SINICA
, vol.16
, pp. 375-390
-
-
KIM, Y.1
KIM, J.2
KIM, Y.3
-
17
-
-
84860650487
-
Sparse recovery in large ensembles of kernel machines
-
R. A. Servedio and T. Zhang, eds, Omnipress, Madison, WI
-
KOLTCHINSKII, V. and YUAN, M. (2008). Sparse recovery in large ensembles of kernel machines. In COLT (R. A. Servedio and T. Zhang, eds.) 229-238. Omnipress, Madison, WI.
-
(2008)
COLT
, pp. 229-238
-
-
KOLTCHINSKII, V.1
YUAN, M.2
-
18
-
-
73949119868
-
-
LEDOUX, M. and TALAGRAND, M. (1991). Probability in Banach Spaces: Isoperimetry and Processes. Springer, Berlin. MR1102015
-
LEDOUX, M. and TALAGRAND, M. (1991). Probability in Banach Spaces: Isoperimetry and Processes. Springer, Berlin. MR1102015
-
-
-
-
19
-
-
33847350805
-
Component selection and smoothing in multivariate nonparametric regression
-
MR2291500
-
LIN, Y. and ZHANG, H. H. (2006). Component selection and smoothing in multivariate nonparametric regression. Ann. Statist. 34 2272-2297. MR2291500
-
(2006)
Ann. Statist
, vol.34
, pp. 2272-2297
-
-
LIN, Y.1
ZHANG, H.H.2
-
20
-
-
0036324753
-
An algorithm for finding protein-DNA binding sites with applications to chromatin-immunoprecipitation microarray experiments
-
LIU, X. S., BRUTLAG, D. L. and LIU, J. S. (2002). An algorithm for finding protein-DNA binding sites with applications to chromatin-immunoprecipitation microarray experiments. Nature Biotechnology 20 835-839.
-
(2002)
Nature Biotechnology
, vol.20
, pp. 835-839
-
-
LIU, X.S.1
BRUTLAG, D.L.2
LIU, J.S.3
-
23
-
-
33747163541
-
High-dimensional graphs and variable selection with the lasso
-
MR2278363
-
MEINSHAUSEN, N. and BÜHLMANN, P. (2006). High-dimensional graphs and variable selection with the lasso. Ann. Statist. 34 1436-1462. MR2278363
-
(2006)
Ann. Statist
, vol.34
, pp. 1436-1462
-
-
MEINSHAUSEN, N.1
BÜHLMANN, P.2
-
24
-
-
65349193793
-
Lasso-type recovery of sparse representations for highdimensional data
-
MR2488351
-
MEINSHAUSEN, N. and YU, B. (2009). Lasso-type recovery of sparse representations for highdimensional data. Ann. Statist. 37 246-270. MR2488351
-
(2009)
Ann. Statist
, vol.37
, pp. 246-270
-
-
MEINSHAUSEN, N.1
YU, B.2
-
26
-
-
85161989288
-
Spam: Sparse additive models
-
J. Platt, D. Koller, Y. Singer and S. Roweis, eds, MIT Press, Cambridge, MA
-
RAVIKUMAR, P., LIU, H., LAFFERTY, J. and WASSERMAN, L. (2008). Spam: Sparse additive models. In Advances in Neural Information Processing Systems 20 (J. Platt, D. Koller, Y. Singer and S. Roweis, eds.) 1201-1208. MIT Press, Cambridge, MA.
-
(2008)
Advances in Neural Information Processing Systems 20
, pp. 1201-1208
-
-
RAVIKUMAR, P.1
LIU, H.2
LAFFERTY, J.3
WASSERMAN, L.4
-
27
-
-
3042574880
-
Amlet, Ramlet, and Gamlet: Automatic nonlinear fitting of additive models, robust and generalized, with wavelets
-
MR2063986
-
SARDY, S. and TSENG, P. (2004). Amlet, Ramlet, and Gamlet: Automatic nonlinear fitting of additive models, robust and generalized, with wavelets. J. Comput. Graph. Statist. 13 283-309. MR2063986
-
(2004)
J. Comput. Graph. Statist
, vol.13
, pp. 283-309
-
-
SARDY, S.1
TSENG, P.2
-
28
-
-
85194972808
-
Regression shrinkage and selection via the lasso
-
MR1379242
-
TIBSHIRANI, R. (1996). Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc. Ser. B 58 267-288. MR1379242
-
(1996)
J. Roy. Statist. Soc. Ser. B
, vol.58
, pp. 267-288
-
-
TIBSHIRANI, R.1
-
30
-
-
51049121146
-
HIGH-dimensional generalized linear models and the lasso
-
MR2396809
-
VAN DE GEER, S. (2008). HIGH-dimensional generalized linear models and the lasso. ANN. STATIST. 36 614-645. MR2396809
-
(2008)
ANN. STATIST
, vol.36
, pp. 614-645
-
-
VAN DE GEER, S.1
-
31
-
-
73949125183
-
-
VAN DER VAART, A. and WELLNER, J. (1996). Weak Convergence and Empirical Processes. Springer, New York. MR1385671
-
VAN DER VAART, A. and WELLNER, J. (1996). Weak Convergence and Empirical Processes. Springer, New York. MR1385671
-
-
-
-
32
-
-
33645035051
-
Model selection and estimation in regression with grouped variables
-
MR2212574
-
YUAN, M. and LIN, Y. (2006). Model selection and estimation in regression with grouped variables. J. R. Stat. Soc. Ser. B Stat. Methodol. 68 49-67. MR2212574
-
(2006)
J. R. Stat. Soc. Ser. B Stat. Methodol
, vol.68
, pp. 49-67
-
-
YUAN, M.1
LIN, Y.2
-
33
-
-
50949096321
-
The sparsity and bias of the lasso selection in highdimensional linear regression
-
MR2435448
-
ZHANG, C.-H. and HUANG, J. (2008). The sparsity and bias of the lasso selection in highdimensional linear regression. Ann. Statist. 36 1567-1594. MR2435448
-
(2008)
Ann. Statist
, vol.36
, pp. 1567-1594
-
-
ZHANG, C.-H.1
HUANG, J.2
-
34
-
-
33846114377
-
The adaptive lasso and its oracle properties
-
MR2279469
-
ZOU, H. (2006). The adaptive lasso and its oracle properties. J. Amer. Statist. Assoc. 101 1418-1429. MR2279469
-
(2006)
J. Amer. Statist. Assoc
, vol.101
, pp. 1418-1429
-
-
ZOU, H.1
|