-
1
-
-
85015823067
-
FDboost: boosting functional regression models
-
Brockhaus, S.: FDboost: boosting functional regression models. R package version 0.0-8, (2015) Available at http://CRAN.R-project.org/package=FDboost
-
(2015)
R package version 0.0-8
-
-
Brockhaus, S.1
-
2
-
-
84930412651
-
The functional linear array model
-
Brockhaus, S., Scheipl, F., Hothorn, T., Greven, S.: The functional linear array model. Stat. Model. 15(3), 279–300 (2015)
-
(2015)
Stat. Model
, vol.15
, Issue.3
, pp. 279-300
-
-
Brockhaus, S.1
Scheipl, F.2
Hothorn, T.3
Greven, S.4
-
3
-
-
41549141939
-
Boosting algorithms: regularization, prediction and model fitting (with discussion)
-
Bühlmann, P., Hothorn, T.: Boosting algorithms: regularization, prediction and model fitting (with discussion). Stat. Sci. 22(4), 477–505 (2007)
-
(2007)
Stat. Sci
, vol.22
, Issue.4
, pp. 477-505
-
-
Bühlmann, P.1
Hothorn, T.2
-
4
-
-
0043245810
-
2 loss: regression and classification
-
2 loss: regression and classification. J. Am. Stat. Assoc. 98(462), 324–339 (2003)
-
(2003)
J. Am. Stat. Assoc
, vol.98
, Issue.462
, pp. 324-339
-
-
Bühlmann, P.1
Yu, B.2
-
5
-
-
0000014224
-
Linear smoothers and additive models
-
Buja, A., Hastie, T.J., Tibshirani, R.J.: Linear smoothers and additive models. Ann. Stat. 17(2), 453–510 (1989)
-
(1989)
Ann. Stat
, vol.17
, Issue.2
, pp. 453-510
-
-
Buja, A.1
Hastie, T.J.2
Tibshirani, R.J.3
-
6
-
-
84879092733
-
Scheipl, F.: refund: Regression with Functional Data
-
Crainiceanu, C.M., Reiss, P.T., Goldsmith, J., Huang, L., Huo, L., Scheipl, F.: refund: Regression with Functional Data. R package version 0.1-12, (2015) Available at https://github.com/refunders/refund
-
(2015)
R package version
, pp. 1-12
-
-
Crainiceanu, C.M.1
Reiss, P.T.2
Goldsmith, J.3
Huang, L.4
Huo, L.5
-
7
-
-
33644769535
-
Generalized linear array models with applications to multidimensional smoothing
-
Currie, I.D., Durban, M., Eilers, P.H.C.: Generalized linear array models with applications to multidimensional smoothing. J. R. Stat. Soc. 68(2), 259–280 (2006)
-
(2006)
J. R. Stat. Soc
, vol.68
, Issue.2
, pp. 259-280
-
-
Currie, I.D.1
Durban, M.2
Eilers, P.H.C.3
-
8
-
-
25444532788
-
Flexible smoothing with B-splines and penalties (with comments and rejoinder)
-
Eilers, P.H.C., Marx, B.D.: Flexible smoothing with B-splines and penalties (with comments and rejoinder). Stat. Sci. 11(2), 89–121 (1996)
-
(1996)
Stat. Sci
, vol.11
, Issue.2
, pp. 89-121
-
-
Eilers, P.H.C.1
Marx, B.D.2
-
9
-
-
0035470889
-
Greedy function approximation: a gradient boosting machine
-
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)
-
(2001)
Ann. Stat
, vol.29
, Issue.5
, pp. 1189-1232
-
-
Friedman, J.H.1
-
10
-
-
84919819684
-
Variable-domain functional regression for modeling ICU data
-
Gellar, J.E., Colantuoni, E., Needham, D.M., Crainiceanu, C.M.: Variable-domain functional regression for modeling ICU data. J. Am. Stat. Assoc. 109(508), 1425–1439 (2014)
-
(2014)
J. Am. Stat. Assoc
, vol.109
, Issue.508
, pp. 1425-1439
-
-
Gellar, J.E.1
Colantuoni, E.2
Needham, D.M.3
Crainiceanu, C.M.4
-
11
-
-
84924367263
-
Dynamic retrospective regression for functional data
-
Gervini, D.: Dynamic retrospective regression for functional data. Technometrics 57(1), 26–34 (2015)
-
(2015)
Technometrics
, vol.57
, Issue.1
, pp. 26-34
-
-
Gervini, D.1
-
12
-
-
34247375568
-
Penalized solutions to functional regression problems
-
Harezlak, J., Coull, B.A., Laird, N.M., Magari, S.R., Christiani, D.C.: Penalized solutions to functional regression problems. Comput. Stat. Data Anal. 51(10), 4911–4925 (2007)
-
(2007)
Comput. Stat. Data Anal
, vol.51
, Issue.10
, pp. 4911-4925
-
-
Harezlak, J.1
Coull, B.A.2
Laird, N.M.3
Magari, S.R.4
Christiani, D.C.5
-
14
-
-
80053581322
-
A framework for unbiased model selection based on boosting
-
Hofner, B., Hothorn, T., Kneib, T., Schmid, M.: A framework for unbiased model selection based on boosting. J. Comput. Graph. Stat. 20(4), 956–971 (2011)
-
(2011)
J. Comput. Graph. Stat
, vol.20
, Issue.4
, pp. 956-971
-
-
Hofner, B.1
Hothorn, T.2
Kneib, T.3
Schmid, M.4
-
15
-
-
84931262233
-
Controlling false discoveries in high-dimensional situations: boosting with stability selection
-
Hofner, B., Boccuto, L., Göker, M.: Controlling false discoveries in high-dimensional situations: boosting with stability selection. BMC Bioinform. 16(1), 144 (2015)
-
(2015)
BMC Bioinform
, vol.16
, Issue.1
, pp. 144
-
-
Hofner, B.1
Boccuto, L.2
Göker, M.3
-
16
-
-
84901420181
-
Hofner, B.: mboost: Model-based boosting
-
Hothorn, T., Bühlmann, P., Kneib, T., Schmid, M., Hofner, B.: mboost: Model-based boosting. R package version 2.4-2, (2015) Available at http://CRAN.R-project.org/package=mboost
-
(2015)
R package version
, vol.2
, pp. 2-4
-
-
Hothorn, T.1
Bühlmann, P.2
Kneib, T.3
Schmid, M.4
-
17
-
-
84930948325
-
Penalized function-on-function regression
-
Ivanescu, A.E., Staicu, A.M., Scheipl, F., Greven, S.: Penalized function-on-function regression. Comput. Stat. 30(2), 539–568 (2015)
-
(2015)
Comput. Stat
, vol.30
, Issue.2
, pp. 539-568
-
-
Ivanescu, A.E.1
Staicu, A.M.2
Scheipl, F.3
Greven, S.4
-
18
-
-
78650221431
-
Recent history functional linear models for sparse longitudinal data
-
Kim, K., Şentürk, D., Li, R.: Recent history functional linear models for sparse longitudinal data. J. Stat. Plan. Inference 141(4), 1554–1566 (2011)
-
(2011)
J. Stat. Plan. Inference
, vol.141
, Issue.4
, pp. 1554-1566
-
-
Kim, K.1
Şentürk, D.2
Li, R.3
-
19
-
-
84868119104
-
Implementation of proton transfer reaction-mass spectrometry (PTR-MS) for advanced bioprocess monitoring
-
Luchner, M., Gutmann, R., Bayer, K., Dunkl, J., Hansel, A., Herbig, J., Singer, W., Strobl, F., Winkler, K., Striedner, G.: Implementation of proton transfer reaction-mass spectrometry (PTR-MS) for advanced bioprocess monitoring. Biotechnol. Bioeng. 109(12), 3059–3069 (2012)
-
(2012)
Biotechnol. Bioeng
, vol.109
, Issue.12
, pp. 3059-3069
-
-
Luchner, M.1
Gutmann, R.2
Bayer, K.3
Dunkl, J.4
Hansel, A.5
Herbig, J.6
Singer, W.7
Strobl, F.8
Winkler, K.9
Striedner, G.10
-
20
-
-
0242595929
-
The historical functional linear model
-
Malfait, N., Ramsay, J.O.: The historical functional linear model. Can. J. Stat. 31(2), 115–128 (2003)
-
(2003)
Can. J. Stat
, vol.31
, Issue.2
, pp. 115-128
-
-
Malfait, N.1
Ramsay, J.O.2
-
21
-
-
79953654016
-
Practical variable selection for generalized additive models
-
Marra, G., Wood, S.N.: Practical variable selection for generalized additive models. Comput. Stat. Data Anal. 55(7), 2372–2387 (2011)
-
(2011)
Comput. Stat. Data Anal
, vol.55
, Issue.7
, pp. 2372-2387
-
-
Marra, G.1
Wood, S.N.2
-
22
-
-
77958487535
-
Stability selection (with discussion)
-
Meinshausen, N., Bühlmann, P.: Stability selection (with discussion). J. R. Stat. Soc. 72(4), 417–473 (2010)
-
(2010)
J. R. Stat. Soc
, vol.72
, Issue.4
, pp. 417-473
-
-
Meinshausen, N.1
Bühlmann, P.2
-
23
-
-
84947045799
-
The potential of random forest and neural networks for biomass and recombinant protein modeling in Escherichia coli fed-batch fermentations
-
Melcher, M., Scharl, T., Spangl, B., Luchner, M., Cserjan, M., Bayer, K., Leisch, F., Striedner, G.: The potential of random forest and neural networks for biomass and recombinant protein modeling in Escherichia coli fed-batch fermentations. Biotechnol. J. 10(11), 1770–1782 (2015)
-
(2015)
Biotechnol. J
, vol.10
, Issue.11
, pp. 1770-1782
-
-
Melcher, M.1
Scharl, T.2
Spangl, B.3
Luchner, M.4
Cserjan, M.5
Bayer, K.6
Leisch, F.7
Striedner, G.8
-
24
-
-
84928111503
-
Functional regression
-
Morris, J.S.: Functional regression. Ann. Rev. Stat. Appl. 2(1), 321–359 (2015)
-
(2015)
Ann. Rev. Stat. Appl
, vol.2
, Issue.1
, pp. 321-359
-
-
Morris, J.S.1
-
26
-
-
84915809885
-
A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
-
R Core Team.: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, R 3.1.2, (2014) Available at http://www.R-project.org/
-
(2014)
R 3
, vol.1
, pp. 2
-
-
Core Team, R.1
-
28
-
-
84964047394
-
Identifiability in penalized function-on-function regression models
-
Scheipl, F., Greven, S.: Identifiability in penalized function-on-function regression models. Electron. J. Stat. 10(1), 495–526 (2016)
-
(2016)
Electron. J. Stat
, vol.10
, Issue.1
, pp. 495-526
-
-
Scheipl, F.1
Greven, S.2
-
29
-
-
84931084660
-
Functional additive mixed models
-
Scheipl, F., Staicu, A.M., Greven, S.: Functional additive mixed models. J. Comput. Graph. Stat. 24(2), 477–501 (2015)
-
(2015)
J. Comput. Graph. Stat
, vol.24
, Issue.2
, pp. 477-501
-
-
Scheipl, F.1
Staicu, A.M.2
Greven, S.3
-
30
-
-
84871371181
-
Variable selection with error control: another look at stability selection
-
Shah, R.D., Samworth, R.J.: Variable selection with error control: another look at stability selection. J. R. Stat. Soc. 75(1), 55–80 (2013)
-
(2013)
J. R. Stat. Soc
, vol.75
, Issue.1
, pp. 55-80
-
-
Shah, R.D.1
Samworth, R.J.2
-
31
-
-
84882974298
-
An advanced monitoring platform for rational design of recombinant processes
-
Mandenius CF, Titchener-Hooker NJ, (eds), Springer, Berlin
-
Striedner, G., Bayer, K.: An advanced monitoring platform for rational design of recombinant processes. In: Mandenius, C.F., Titchener-Hooker, N.J. (eds.) Measurement, Monitoring, Modelling and Control of Bioprocesses, pp. 65–84. Springer, Berlin (2013)
-
(2013)
Measurement, Monitoring, Modelling and Control of Bioprocesses
, pp. 65-84
-
-
Striedner, G.1
Bayer, K.2
-
32
-
-
77749280569
-
Feature extraction in signal regression: a boosting technique for functional data regression
-
Tutz, G., Gertheiss, J.: Feature extraction in signal regression: a boosting technique for functional data regression. J. Comput. Graph. Stat. 19(1), 154–174 (2010)
-
(2010)
J. Comput. Graph. Stat
, vol.19
, Issue.1
, pp. 154-174
-
-
Tutz, G.1
Gertheiss, J.2
|