-
1
-
-
0016355478
-
A New Look at the Statistical Model Identification
-
H.Akaike, (1974), “A New Look at the Statistical Model Identification,” IEEE Transactions on Automatic Control, 19, 716--723.
-
(1974)
IEEE Transactions on Automatic Control
, vol.19
, pp. 716-723
-
-
Akaike, H.1
-
2
-
-
0016029778
-
The Relationship Between Variable Selection and Data Argumentation and a Method of Prediction
-
D.M.Allen, (1974), “The Relationship Between Variable Selection and Data Argumentation and a Method of Prediction,” Technometrics, 16, 125–127.
-
(1974)
Technometrics
, vol.16
, pp. 125-127
-
-
Allen, D.M.1
-
5
-
-
68649086910
-
Simultaneous Analysis of Lasso and Dantzig Selector
-
P.Bickel,, Y.Ritov,, and A.Tsybakov, (2009), “Simultaneous Analysis of Lasso and Dantzig Selector,” Annals of Statistics, 37, 1705–1732.
-
(2009)
Annals of Statistics
, vol.37
, pp. 1705-1732
-
-
Bickel, P.1
Ritov, Y.2
Tsybakov, A.3
-
6
-
-
0038368335
-
Stability and Generalization
-
O.Bousquet,, and A.Elisseeff, (2002), “Stability and Generalization,” Journal of Machine Learning Research, 2, 499–426.
-
(2002)
Journal of Machine Learning Research
, vol.2
, pp. 426-499
-
-
Bousquet, O.1
Elisseeff, A.2
-
7
-
-
84874257732
-
Better Subset Regression Using the Nonnegative Garrote
-
L.Breiman, (1995), “Better Subset Regression Using the Nonnegative Garrote,” Technometrics, 37373--384.
-
(1995)
Technometrics
, pp. 373-384
-
-
Breiman, L.1
-
8
-
-
0030344230
-
Heuristics of Instability and Stabilization in Model Selection
-
L.Breiman, (1996), “Heuristics of Instability and Stabilization in Model Selection,” The Annals of Statistics, 24, 2350–2383.
-
(1996)
The Annals of Statistics
, vol.24
, pp. 2350-2383
-
-
Breiman, L.1
-
9
-
-
0000245743
-
Statistical Modeling: The Two Cultures
-
L.Breiman, (2001), “Statistical Modeling: The Two Cultures,” Statistical Science, 16, 199–231.
-
(2001)
Statistical Science
, vol.16
, pp. 199-231
-
-
Breiman, L.1
-
10
-
-
0043245810
-
Boosting with the L2 Loss: Regression and Classification
-
P.Bühlmann,, and B.Yu, (2003), “Boosting with the L2 Loss: Regression and Classification,” Journal of the American Statistical Association, 98, 324–339.
-
(2003)
Journal of the American Statistical Association
, vol.98
, pp. 324-339
-
-
Bühlmann, P.1
Yu, B.2
-
11
-
-
50949108781
-
Extended Bayesian Information Criteria for Model Selection With Large Model Spaces
-
J.Chen,, and Z.Chen, (2008), “Extended Bayesian Information Criteria for Model Selection With Large Model Spaces,” Biometrika, 95, 759–771.
-
(2008)
Biometrika
, vol.95
, pp. 759-771
-
-
Chen, J.1
Chen, Z.2
-
12
-
-
0002344794
-
Bootstrap Methods: Another Look at the Jackknife
-
B.Efron, (1979), “Bootstrap Methods: Another Look at the Jackknife,” The Annals of Statistics, 7, 1–26.
-
(1979)
The Annals of Statistics
, vol.7
, pp. 1-26
-
-
Efron, B.1
-
13
-
-
3242708140
-
Least Angle Regression
-
B.Efron,, T.Hastie,, I.Johnstone,, and R.Tibshirani, (2004), “Least Angle Regression,” The Annals of Statistics, 32, 407–499.
-
(2004)
The Annals of Statistics
, vol.32
, pp. 407-499
-
-
Efron, B.1
Hastie, T.2
Johnstone, I.3
Tibshirani, R.4
-
14
-
-
0001041317
-
Instability of Least Squares, Least Absolute Deviation and Least Median of Squares Linear Regression
-
S.P.Ellis, (1998), “Instability of Least Squares, Least Absolute Deviation and Least Median of Squares Linear Regression,” Statistical Science, 13, 337–350.
-
(1998)
Statistical Science
, vol.13
, pp. 337-350
-
-
Ellis, S.P.1
-
15
-
-
77950537175
-
Regularization Paths for Generalized Linear Models via Coordinate Descent
-
J.Friedman,, T.Hastie,, and R.Tibshirani, (2010), “Regularization Paths for Generalized Linear Models via Coordinate Descent,” Journal of Statistical Software, 33, 1–22.
-
(2010)
Journal of Statistical Software
, vol.33
, pp. 1-22
-
-
Friedman, J.1
Hastie, T.2
Tibshirani, R.3
-
16
-
-
0035470889
-
Greedy Function Approximation: A Gradient Boosting Machine
-
J.H.Friedman, (2001), “Greedy Function Approximation: A Gradient Boosting Machine,” The Annals of Statistics, 29, 1189–1232.
-
(2001)
The Annals of Statistics
, vol.29
, pp. 1189-1232
-
-
Friedman, J.H.1
-
17
-
-
0003684449
-
-
New York, NY: Springer
-
T.Hastie,, R.Tibshirani,, and J.Friedman, (2002), The Elements of Statistical Learning: Data Mining, Inference, and Prediction, New York, NY: Springer.
-
(2002)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
-
-
Hastie, T.1
Tibshirani, R.2
Friedman, J.3
-
18
-
-
84869882656
-
TIGRESS: Trustful Inference of Gene REgulation using Stability Selection
-
A.-C.Haury,, F.Mordelet,, P.Vera-Licona,, and J.-P.Vert, (2012), “TIGRESS: Trustful Inference of Gene REgulation using Stability Selection,” BMC Systems Biology, 6.
-
(2012)
BMC Systems Biology
, pp. 6
-
-
Haury, A.-C.1
Mordelet, F.2
Vera-Licona, P.3
Vert, J.-P.4
-
20
-
-
0002161961
-
Application of Ridge Analysis to Regression Problems
-
A.Hoerl, (1962), “Application of Ridge Analysis to Regression Problems,” Chemical Engineering Progress58.
-
(1962)
Chemical Engineering Progress
, vol.58
-
-
Hoerl, A.1
-
21
-
-
70349119250
-
Regression and Time Series Model Selection in Small Samples
-
C.Hurvich,, and C.-L.Tsai, (1989), “Regression and Time Series Model Selection in Small Samples,” Biometrika, 76, 297–307.
-
(1989)
Biometrika
, vol.76
, pp. 297-307
-
-
Hurvich, C.1
Tsai, C.-L.2
-
22
-
-
41149148634
-
Identifying Natural Images from Human Brain Activity
-
K.Kay,, T.Naselaris,, R.Prenger,, and J.Gallant, (2008), “Identifying Natural Images from Human Brain Activity,” Nature, 452, 352–355.
-
(2008)
Nature
, vol.452
, pp. 352-355
-
-
Kay, K.1
Naselaris, T.2
Prenger, R.3
Gallant, J.4
-
25
-
-
33846193774
-
A Note on the Lasso and Related Procedures in Model Selection
-
C.Leng,, Y.Lin,, and G.Wahba, (2006), “A Note on the Lasso and Related Procedures in Model Selection,” Statistica Sinica, 16, 1273–1284.
-
(2006)
Statistica Sinica
, vol.16
, pp. 1273-1284
-
-
Leng, C.1
Lin, Y.2
Wahba, G.3
-
26
-
-
85162067025
-
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models
-
H.Liu,, K.Roeder,, and L.Wasserman, (2010), “Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models,” Advances in Neural Information Processing Systems, 23.
-
(2010)
Advances in Neural Information Processing Systems
, pp. 23
-
-
Liu, H.1
Roeder, K.2
Wasserman, L.3
-
27
-
-
84867135375
-
-
J.Mairal,, and B.Yu, (2012), “Complexity Analysis of the Lasso Regularization Path,” in Proceedings of the 29th International Conference on Machine Learning (ICML), Edinburgh, Scotland, UK, pp. 353--360.
-
(2012)
Complexity Analysis of the Lasso Regularization Path
-
-
Mairal, J.1
Yu, B.2
-
28
-
-
33747163541
-
High-Dimensional Graphs and Variable Selection with the Lasso
-
J.Mairal, (2006), “High-Dimensional Graphs and Variable Selection with the Lasso,” The Annals of Statistics, 34, 1436–1462.
-
(2006)
The Annals of Statistics
, vol.34
, pp. 1436-1462
-
-
Mairal, J.1
-
29
-
-
77958487535
-
Stability Selection
-
N.Meinshausen,, and P.Bühlmann, (2010), “Stability Selection,” Journal of the Royal Statistical Society, Series B, 72, 417--473.
-
(2010)
Journal of the Royal Statistical Society, Series B, 72, 417--473
-
-
Meinshausen, N.1
Bühlmann, P.2
-
30
-
-
50849120401
-
Discussion: A Tale of Three Cousins: Lasso, L2Boosting and Dantzig
-
N.Meinshausen,, G.Rocha,, and B.Yu, (2007), “Discussion: A Tale of Three Cousins: Lasso, L2Boosting and Dantzig,” The Annals of Statistics, 35, 2373–2384.
-
(2007)
The Annals of Statistics
, vol.35
, pp. 2373-2384
-
-
Meinshausen, N.1
Rocha, G.2
Yu, B.3
-
31
-
-
65349193793
-
Lasso-Type Recovery of Sparse Representations for High-Dimensional Data
-
N.Meinshausen,, and B.Yu, (2009), “Lasso-Type Recovery of Sparse Representations for High-Dimensional Data,” Annals of Statistics, 37, 246–270.
-
(2009)
Annals of Statistics
, vol.37
, pp. 246-270
-
-
Meinshausen, N.1
Yu, B.2
-
32
-
-
33745655665
-
Learning Theory: Stability is Sufficient for Generalization and Necessary and Sufficient for Consistency of Empirical Risk Minimization
-
S.Mukherjee,, P.Niyogi,, T.Poggio,, and R.Rifkin, (2006), “Learning Theory: Stability is Sufficient for Generalization and Necessary and Sufficient for Consistency of Empirical Risk Minimization,” Advances in Computational Mathematics, 26, 161–193.
-
(2006)
Advances in Computational Mathematics
, vol.26
, pp. 161-193
-
-
Mukherjee, S.1
Niyogi, P.2
Poggio, T.3
Rifkin, R.4
-
33
-
-
84925959153
-
Variable Selection Diagnostics Measures for High-Dimensional Regression
-
Y.Nan,, and Y.Yang, (2014), “Variable Selection Diagnostics Measures for High-Dimensional Regression,” Journal of Computational and Graphical Statistics, 23, 636–656.
-
(2014)
Journal of Computational and Graphical Statistics
, vol.23
, pp. 636-656
-
-
Nan, Y.1
Yang, Y.2
-
34
-
-
33746477970
-
Identification of Signaling Components Required for the Prediction of Cytokine Release in RAW 264.7 Macrophages
-
S.Pradervand,, M.Maurya,, and S.Subramaniam, (2006), “Identification of Signaling Components Required for the Prediction of Cytokine Release in RAW 264.7 Macrophages,” Genome Biology, 7.
-
(2006)
Genome Biology
, pp. 7
-
-
Pradervand, S.1
Maurya, M.2
Subramaniam, S.3
-
36
-
-
84871371181
-
Variable Selection With Error Control: Another Look at Stability Selection
-
R.D.Shah,, and R.J.Samworth, (2013), “Variable Selection With Error Control: Another Look at Stability Selection,” Journal of the Royal Statistical Society, Series B, 75, 55–80.
-
(2013)
Journal of the Royal Statistical Society, Series B
, vol.75
, pp. 55-80
-
-
Shah, R.D.1
Samworth, R.J.2
-
39
-
-
0000859675
-
Cross-Validation Choice and Assessment of Statistical Prediction
-
M.Stone, (1974), “Cross-Validation Choice and Assessment of Statistical Prediction,” Journal of the Royal Statistical Society, Series B, 39, 44–47.
-
(1974)
Journal of the Royal Statistical Society, Series B
, vol.39
, pp. 44-47
-
-
Stone, M.1
-
40
-
-
85194972808
-
Regression Shrinkage and Selection via the Lasso
-
R.Tibshirani, (1996), “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society, Series B, 58, 267–288.
-
(1996)
Journal of the Royal Statistical Society, Series B
, vol.58
, pp. 267-288
-
-
Tibshirani, R.1
-
41
-
-
0000418073
-
On the Stability of Inverse Problems
-
A.N.Tikhonov, (1943), “On the Stability of Inverse Problems,” Doklady Akademii Nauk SSSR, 39, 195–198.
-
(1943)
Doklady Akademii Nauk SSSR
, vol.39
, pp. 195-198
-
-
Tikhonov, A.N.1
-
42
-
-
33645712308
-
Just Relax: Convex Programming Methods for Identifying Sparse Signals in Noise
-
J.Tropp, (2006), “Just Relax: Convex Programming Methods for Identifying Sparse Signals in Noise,” IEEE Transactions on Information Theory, 52.
-
(2006)
IEEE Transactions on Information Theory
, pp. 52
-
-
Tropp, J.1
-
44
-
-
69049091975
-
High-Dimensional Variable Selection
-
L.Wasserman,, and K.Roeder, (2009), “High-Dimensional Variable Selection,” The Annals of Statistics, 37, 2178–2201.
-
(2009)
The Annals of Statistics
, vol.37
, pp. 2178-2201
-
-
Wasserman, L.1
Roeder, K.2
-
45
-
-
39649100346
-
Consistency of Cross Validation for Comparing Regression Procedures
-
Y.Yang, (2007), “Consistency of Cross Validation for Comparing Regression Procedures,” The Annals of Statistics, 35, 2450–2473.
-
(2007)
The Annals of Statistics
, vol.35
, pp. 2450-2473
-
-
Yang, Y.1
-
46
-
-
84885074997
-
Stability
-
B.Yu, (2013), “Stability,” Bernoulli, 19, 1484–1500.
-
(2013)
Bernoulli
, vol.19
, pp. 1484-1500
-
-
Yu, B.1
-
47
-
-
29144505412
-
Combining Linear Regression Models: When and How?
-
Z.Yuan,, and Y.Yang, (2005), “Combining Linear Regression Models: When and How?,” Journal of the American Statistical Association, 100, 1202–1214.
-
(2005)
Journal of the American Statistical Association
, vol.100
, pp. 1202-1214
-
-
Yuan, Z.1
Yang, Y.2
-
48
-
-
50949096321
-
The Sparsity and Bias of the Lasso Selection in High-Dimensional Linear Regression
-
C.-H.Zhang,, and J.Huang, (2008), “The Sparsity and Bias of the Lasso Selection in High-Dimensional Linear Regression,” The Annals of Statistics, 36, 1567–1594.
-
(2008)
The Annals of Statistics
, vol.36
, pp. 1567-1594
-
-
Zhang, C.-H.1
Huang, J.2
-
49
-
-
21144472438
-
Model Selection Via Multifold Cross-validation
-
P.Zhang, (1993), “Model Selection Via Multifold Cross-validation,” The Annals of Statistics, 21, 299–313.
-
(1993)
The Annals of Statistics
, vol.21
, pp. 299-313
-
-
Zhang, P.1
-
50
-
-
79959549699
-
Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations
-
T.Zhang, (2011), “Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations,” IEEE Transactions on Information Theory, 57, 4689–4708.
-
(2011)
IEEE Transactions on Information Theory
, vol.57
, pp. 4689-4708
-
-
Zhang, T.1
-
51
-
-
26444493144
-
Boosting With Early Stopping: Convergence and Consistency
-
T.Zhang,, and B.Yu, (2005), “Boosting With Early Stopping: Convergence and Consistency,” The Annals of Statistics, 33, 1538–1579.
-
(2005)
The Annals of Statistics
, vol.33
, pp. 1538-1579
-
-
Zhang, T.1
Yu, B.2
-
52
-
-
33845263263
-
On Model Selection Consistency of Lasso
-
P.Zhao,, and B.Yu, (2006), “On Model Selection Consistency of Lasso,” Journal of Machine Learning Research, 7, 2541–2563.
-
(2006)
Journal of Machine Learning Research
, vol.7
, pp. 2541-2563
-
-
Zhao, P.1
Yu, B.2
|