-
1
-
-
33745156863
-
Some theory of Fisher's linear discriminant function, "naive Bayes," and some alternatives where there are many more variables than observations
-
MR2108040
-
BICKEL, P. and LEVINA, E. (2004). Some theory of Fisher's linear discriminant function, "naive Bayes," and some alternatives where there are many more variables than observations. Bernoulli 10 989-1010. MR2108040
-
(2004)
Bernoulli
, vol.10
, pp. 989-1010
-
-
BICKEL, P.1
LEVINA, E.2
-
2
-
-
0000245743
-
Statistical modeling: The two cultures (with discussion)
-
MR1874152
-
BREIMAN, L. (2001). Statistical modeling: The two cultures (with discussion). Statist. Sci. 16 199-231. MR1874152
-
(2001)
Statist. Sci
, vol.16
, pp. 199-231
-
-
BREIMAN, L.1
-
3
-
-
14644395930
-
Population theory for boosting ensembles
-
MR2050998
-
BREIMAN, L. (2004). Population theory for boosting ensembles. Ann. Statist. 32 1-11. MR2050998
-
(2004)
Ann. Statist
, vol.32
, pp. 1-11
-
-
BREIMAN, L.1
-
4
-
-
33847360949
-
Discussion of boosting papers
-
BÜHLMANN, P. and BIN, Y. (2004). Discussion of boosting papers. Ann. Statist. 32 96-101.
-
(2004)
Ann. Statist
, vol.32
, pp. 96-101
-
-
BÜHLMANN, P.1
BIN, Y.2
-
5
-
-
0035273106
-
Atomic decomposition by basis pursuit
-
MR1854649
-
CHEN, S., DONOHO, D. and SAUNDERS, M. (2001). Atomic decomposition by basis pursuit. SIAM Rev. 43 129-159. MR1854649
-
(2001)
SIAM Rev
, vol.43
, pp. 129-159
-
-
CHEN, S.1
DONOHO, D.2
SAUNDERS, M.3
-
6
-
-
33847363114
-
-
1-norm solution is also the sparsest solution. Technical Report 2004-9, Dept. Statistics, Stanford Univ.
-
1-norm solution is also the sparsest solution. Technical Report 2004-9, Dept. Statistics, Stanford Univ.
-
-
-
-
7
-
-
33847351610
-
-
1-norm near-solution approximates the sparsest near-solution. Technical Report 2004-10, Dept. Statistics, Stanford Univ.
-
1-norm near-solution approximates the sparsest near-solution. Technical Report 2004-10, Dept. Statistics, Stanford Univ.
-
-
-
-
8
-
-
3242708140
-
Least angle regression (with discussion)
-
MR2060166
-
EFRON, B., JOHNSTONE, I., HASTIE, T. and TIBSHIRANI, R. (2004). Least angle regression (with discussion). Ann. Statist. 32 407-499. MR2060166
-
(2004)
Ann. Statist
, vol.32
, pp. 407-499
-
-
EFRON, B.1
JOHNSTONE, I.2
HASTIE, T.3
TIBSHIRANI, R.4
-
9
-
-
1542784498
-
Variable selection via nonconcave penalized likelihood and its oracle properties
-
MR1946581
-
FAN, J. and LI, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. J. Amer. Statist. Assoc. 96 1348-1360. MR1946581
-
(2001)
J. Amer. Statist. Assoc
, vol.96
, pp. 1348-1360
-
-
FAN, J.1
LI, R.2
-
10
-
-
24344502730
-
Nonconcave penalized likelihood with a diverging number of parameters
-
MR2065194
-
FAN, J. and PENG, H. (2004). Nonconcave penalized likelihood with a diverging number of parameters. Ann. Statist. 32 928-961. MR2065194
-
(2004)
Ann. Statist
, vol.32
, pp. 928-961
-
-
FAN, J.1
PENG, H.2
-
11
-
-
3142617128
-
Discussion of boosting papers
-
FRIEDMAN, J., HASTIE, T., ROSSET, S., TIBSHIRANI, R. and ZHU, J. (2004). Discussion of boosting papers. Ann. Statist. 32 102-107.
-
(2004)
Ann. Statist
, vol.32
, pp. 102-107
-
-
FRIEDMAN, J.1
HASTIE, T.2
ROSSET, S.3
TIBSHIRANI, R.4
ZHU, J.5
-
12
-
-
31344477296
-
Prediction, model selection and random dimension penalties
-
MR2203888
-
GREENSHTEIN, E. (2005). Prediction, model selection and random dimension penalties. Sankhyā 67 46-73. MR2203888
-
(2005)
Sankhyā
, vol.67
, pp. 46-73
-
-
GREENSHTEIN, E.1
-
13
-
-
31344454903
-
Persistence in high-dimensional predictor selection and the virtue of overparametrization
-
MR2108039
-
GREENSHTEIN, E. and RITOV, Y. (2004). Persistence in high-dimensional predictor selection and the virtue of overparametrization. Bernoulli 10 971-988. MR2108039
-
(2004)
Bernoulli
, vol.10
, pp. 971-988
-
-
GREENSHTEIN, E.1
RITOV, Y.2
-
14
-
-
33847365722
-
-
HASTIE, T., TIBSHIRANI, R. and FRIEDMAN, J. (2001). The Elements of Statistical Learning. Data Mining, Inference and Prediction. Springer, New York. MR1851606
-
HASTIE, T., TIBSHIRANI, R. and FRIEDMAN, J. (2001). The Elements of Statistical Learning. Data Mining, Inference and Prediction. Springer, New York. MR1851606
-
-
-
-
15
-
-
0001033261
-
Robust regression: Asymptotics, conjectures, and Monte Carlo
-
MR0356373
-
HUBER, P. (1973). Robust regression: Asymptotics, conjectures, and Monte Carlo. Ann. Statist. 1 799-821. MR0356373
-
(1973)
Ann. Statist
, vol.1
, pp. 799-821
-
-
HUBER, P.1
-
16
-
-
0034362823
-
Functional aggregation for nonparametric regression
-
MR1792783
-
JUDITSKY, A. and NEMIROVSKI, A. (2000). Functional aggregation for nonparametric regression. Ann. Statist. 28681-712. MR1792783
-
(2000)
Ann. Statist
, vol.28
, pp. 681-712
-
-
JUDITSKY, A.1
NEMIROVSKI, A.2
-
17
-
-
0001556720
-
Efficient agnostic learning of neural networks with bounded fan-in
-
MR1447518
-
LEE, W. S., BARTLETT, P. L. and WILLIAMSON, R. C. (1996). Efficient agnostic learning of neural networks with bounded fan-in. IEEE Trans. Inform. Theory 42 2118-2132. MR1447518
-
(1996)
IEEE Trans. Inform. Theory
, vol.42
, pp. 2118-2132
-
-
LEE, W.S.1
BARTLETT, P.L.2
WILLIAMSON, R.C.3
-
18
-
-
9444269961
-
On the Bayes-risk consistency of regularized boosting methods
-
MR2051000
-
LUGOSI, G. and VAYATIS, N. (2004). On the Bayes-risk consistency of regularized boosting methods. Ann. Statist. 32 30-55. MR2051000
-
(2004)
Ann. Statist
, vol.32
, pp. 30-55
-
-
LUGOSI, G.1
VAYATIS, N.2
-
19
-
-
33747163541
-
High-dimensional graphs and variable selection with the Lasso
-
MEINSHAUSEN, N. and BÜHLMANN, P. (2006). High-dimensional graphs and variable selection with the Lasso. Ann. Statist. 34 1436-1462.
-
(2006)
Ann. Statist
, vol.34
, pp. 1436-1462
-
-
MEINSHAUSEN, N.1
BÜHLMANN, P.2
-
20
-
-
33847358145
-
-
NEMIROVSKI, A. and YUDIN, D. (1983). Problem Complexity and Method Efficiency in Optimization. Wiley, New York. MR0702836
-
NEMIROVSKI, A. and YUDIN, D. (1983). Problem Complexity and Method Efficiency in Optimization. Wiley, New York. MR0702836
-
-
-
-
21
-
-
12244284624
-
DNA microarray experiments: Biological and technological aspects
-
MR1939398
-
NGUYEN, D. V., ARPAT, A. B., WANG, N. and CARROLL, R. J. (2002). DNA microarray experiments: Biological and technological aspects. Biometrics 58 701-717. MR1939398
-
(2002)
Biometrics
, vol.58
, pp. 701-717
-
-
NGUYEN, D.V.1
ARPAT, A.B.2
WANG, N.3
CARROLL, R.J.4
-
22
-
-
33847409243
-
-
PISIER, G. (1981). Remarques sur un résultat non publié de B, Maurey. In Seminaire d'Analyse Fonctionelle 112. École Polytechnique, Palaiseau. MR0659306
-
PISIER, G. (1981). Remarques sur un résultat non publié de B, Maurey. In Seminaire d'Analyse Fonctionelle 112. École Polytechnique, Palaiseau. MR0659306
-
-
-
-
23
-
-
0000981525
-
2/n is large. I. Consistency
-
MR0760690
-
2/n is large. I. Consistency. Ann. Statist. 12 1298-1309. MR0760690
-
(1984)
Ann. Statist
, vol.12
, pp. 1298-1309
-
-
PORTNOY, S.1
-
24
-
-
0001287271
-
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
-
25
-
-
33847359102
-
-
VAPNIK, N. V. (1998). Statistical Learning Theory. Wiley, New York. MR1641250
-
VAPNIK, N. V. (1998). Statistical Learning Theory. Wiley, New York. MR1641250
-
-
-
-
26
-
-
0000007831
-
Asymptotic behavior of M-estimators for the linear model
-
MR0520237
-
YOHAI, V. J. and MARONNA, R. A. (1979). Asymptotic behavior of M-estimators for the linear model. Ann. Statist. 7 258-268. MR0520237
-
(1979)
Ann. Statist
, vol.7
, pp. 258-268
-
-
YOHAI, V.J.1
MARONNA, R.A.2
|