Loading
Error: Cannot Load Popup Box
Skip to hit list
Adjust your hit list
Further result pages
Mobile

A
A
A

A

English
Deutsch
Français
Español
Polski
Ελληνικά
Українська
中文
 Logged in as

Log Out

Login
BASIC
SEARCH
ADVANCED
SEARCH
HELP
BROWSING
SEARCH
HISTORY
Your search
Search For:
Entire Document
Title
Author
Subject
Boost open access documents
Find
Linguistics tools
Verbatim search
Additional word forms
Multilingual synonyms
Statistics
43 hits
in 72,227,055 documents
in 0.22 seconds
Please leave the following field blank:
Home
»
Search: Benjamin Hofner
Hit List
Hit list
1.
ModelBased Boosting: Unbiased Variable Selection and Model Choice
Open Access
Title:
ModelBased Boosting: Unbiased Variable Selection and Model Choice
Author:
Benjamin Hofner
Benjamin Hofner
Minimize authors
Description:
Variable selection and model choice are of major concern in many applications, especially in highdimensional settings. Boosting (for an overview see Bühlmann and Hothorn (2007)) is a useful method for model fitting with intrinsic variable selection and model choice. However, a central problem remains: Variable selection is biased if the covaria...
Variable selection and model choice are of major concern in many applications, especially in highdimensional settings. Boosting (for an overview see Bühlmann and Hothorn (2007)) is a useful method for model fitting with intrinsic variable selection and model choice. However, a central problem remains: Variable selection is biased if the covariates are of very different nature. An important example is given by models that try to make use of continuous and categorical covariates at the same time. Especially if the number of categories increases, categorical covariates offer an increased flexibility and thus are preferred over continuous covariates (with linear effects). A closely related problem is model choice, where one tries to choose between different modeling alternatives for one covariate. The choice between linear or smooth effects is a classical example. The two competitors have different degrees of freedom (1 df for the linear effect and considerably more than 1 df for the smooth effect). Hence, smooth effects are preferably selected. To make categorical covariates comparable to linear effects in the boosting framework one could use ridge penalized baselearners (i.e, modeling components) with 1 df in this case. To overcome the problem of different degrees of freedom of, e.g., linear and smooth effects Kneib
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20110316
Source:
http://www.stat.unimuenchen.de/%7Emahling/Kolloquium/ss09/090610_Hofner.pdf
http://www.stat.unimuenchen.de/%7Emahling/Kolloquium/ss09/090610_Hofner.pdf
Minimize
Document Type:
text
Language:
en
DDC:
310 Collections of general statistics
(computed)
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.183.6444
http://www.stat.unimuenchen.de/%7Emahling/Kolloquium/ss09/090610_Hofner.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.183.6444
http://www.stat.unimuenchen.de/%7Emahling/Kolloquium/ss09/090610_Hofner.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
2.
gamboostLSS: boosting generalized additive models for location, scale
Open Access
Title:
gamboostLSS: boosting generalized additive models for location, scale
Author:
Benjamin Hofner
Benjamin Hofner
Minimize authors
Description:
and shape
and shape
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20131106
Source:
http://web.warwick.ac.uk/statsdept/useR2011/abstracts/010411
hofner
benjamin.pdf
http://web.warwick.ac.uk/statsdept/useR2011/abstracts/010411
hofner
benjamin.pdf
Minimize
Document Type:
text
Language:
en
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.374.2665
http://web.warwick.ac.uk/statsdept/useR2011/abstracts/010411hofnerbenjamin.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.374.2665
http://web.warwick.ac.uk/statsdept/useR2011/abstracts/010411hofnerbenjamin.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
3.
License GPL2 Repository CRAN
Open Access
Title:
License GPL2 Repository CRAN
Author:
Benjamin Hofner
;
Andreas Mayr
;
Nora Fenske
;
Matthias Schmid
;
Maintainer Benjamin Hofner
;
Needscompilation No
Benjamin Hofner
;
Andreas Mayr
;
Nora Fenske
;
Matthias Schmid
;
Maintainer Benjamin Hofner
;
Needscompilation No
Minimize authors
Description:
Description Boosting models for fitting generalized additive models for location, shape and scale (gamLSS models) to potentially high dimensional data.
Description Boosting models for fitting generalized additive models for location, shape and scale (gamLSS models) to potentially high dimensional data.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20131010
Source:
http://cran.at.rproject.org/web/packages/gamboostLSS/gamboostLSS.pdf
http://cran.at.rproject.org/web/packages/gamboostLSS/gamboostLSS.pdf
Minimize
Document Type:
text
Language:
en
Subjects:
LazyLoad yes
LazyLoad yes
Minimize
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.364.9477
http://cran.at.rproject.org/web/packages/gamboostLSS/gamboostLSS.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.364.9477
http://cran.at.rproject.org/web/packages/gamboostLSS/gamboostLSS.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
4.
Variable Selection and Model Choice in Structured Survival Models
Open Access
Title:
Variable Selection and Model Choice in Structured Survival Models
Author:
Benjamin Hofner
;
Torsten Hothorn
;
Thomas Kneib
;
Benjamin Hofner
;
Torsten Hothorn
;
Thomas Kneib
Benjamin Hofner
;
Torsten Hothorn
;
Thomas Kneib
;
Benjamin Hofner
;
Torsten Hothorn
;
Thomas Kneib
Minimize authors
Description:
In many situations, medical applications ask for flexible survival models that allow to extend the classical Coxmodel via the inclusion of timevarying and nonparametric effects. These structured survival models are very flexible but additional difficulties arise when model choice and variable selection is desired. In particular, it has to be d...
In many situations, medical applications ask for flexible survival models that allow to extend the classical Coxmodel via the inclusion of timevarying and nonparametric effects. These structured survival models are very flexible but additional difficulties arise when model choice and variable selection is desired. In particular, it has to be decided which covariates should be assigned timevarying effects or whether parametric modeling is sufficient for a given covariate. Componentwise boosting provides a means of likelihoodbased model fitting that enables simultaneous variable selection and model choice. We introduce a componentwise likelihoodbased boosting algorithm for survival data that permits the inclusion of both parametric and nonparametric timevarying effects as well as nonparametric effects of continuous covariates utilizing penalized splines as the main modeling technique. Its properties and performance are investigated in simulation studies. The new modeling approach is used to build a flexible survival model for intensive care patients suffering from severe sepsis. A software implementation is available to the interested reader.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20150111
Source:
http://epub.ub.unimuenchen.de/7901/1/TR043.pdf
http://epub.ub.unimuenchen.de/7901/1/TR043.pdf
Minimize
Document Type:
text
Language:
en
Subjects:
Key words ; likelihoodbased boosting ; hazard regression ; model choice ; Psplines ; smooth effects
Key words ; likelihoodbased boosting ; hazard regression ; model choice ; Psplines ; smooth effects
Minimize
DDC:
310 Collections of general statistics
(computed)
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.526.9743
http://epub.ub.unimuenchen.de/7901/1/TR043.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.526.9743
http://epub.ub.unimuenchen.de/7901/1/TR043.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
5.
This is an extended and slightly modified version of the manuscript
Open Access
Title:
This is an extended and slightly modified version of the manuscript
Author:
Benjamin Hofner
;
Andreas Mayr
;
Nikolay Robinzonov
;
Matthias Schmid
;
Benjamin Hofner
;
Andreas Mayr
;
Nikolay Robinzonov
;
Mattthias Schmid
Benjamin Hofner
;
Andreas Mayr
;
Nikolay Robinzonov
;
Matthias Schmid
;
Benjamin Hofner
;
Andreas Mayr
;
Nikolay Robinzonov
;
Mattthias Schmid
Minimize authors
Description:
We provide a detailed handson tutorial for the R addon package mboost. The package implements boosting for optimizing general risk functions utilizing componentwise (penalized) least squares estimates as baselearners for fitting various kinds of generalized linear and generalized additive models to potentially highdimensional data. We give ...
We provide a detailed handson tutorial for the R addon package mboost. The package implements boosting for optimizing general risk functions utilizing componentwise (penalized) least squares estimates as baselearners for fitting various kinds of generalized linear and generalized additive models to potentially highdimensional data. We give a theoretical background and demonstrate how mboost can be used to fit interpretable models of different complexity. As an example we use mboost to predict the body fat based on anthropometric measurements throughout the tutorial. 1
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20120514
Source:
http://cran.rproject.org/web/packages/mboost/vignettes/mboost_tutorial.pdf
http://cran.rproject.org/web/packages/mboost/vignettes/mboost_tutorial.pdf
Minimize
Document Type:
text
Language:
en
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.225.8383
http://cran.rproject.org/web/packages/mboost/vignettes/mboost_tutorial.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.225.8383
http://cran.rproject.org/web/packages/mboost/vignettes/mboost_tutorial.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
6.
This is an extended and slightly modified version of the manuscript
Open Access
Title:
This is an extended and slightly modified version of the manuscript
Author:
Benjamin Hofner
;
Andreas Mayr
;
Nikolay Robinzonov
;
Matthias Schmid
;
Benjamin Hofner
;
Andreas Mayr
;
Nikolay Robinzonov
;
Mattthias Schmid
Benjamin Hofner
;
Andreas Mayr
;
Nikolay Robinzonov
;
Matthias Schmid
;
Benjamin Hofner
;
Andreas Mayr
;
Nikolay Robinzonov
;
Mattthias Schmid
Minimize authors
Description:
Changes to the results in the original manuscript are due to changes in some defaults of mboost (most notably the definition of degrees of freedom has changed in mboost 2.20). See NEWS for details. We provide a detailed handson tutorial for the R addon package mboost. The package implements boosting for optimizing general risk functions utili...
Changes to the results in the original manuscript are due to changes in some defaults of mboost (most notably the definition of degrees of freedom has changed in mboost 2.20). See NEWS for details. We provide a detailed handson tutorial for the R addon package mboost. The package implements boosting for optimizing general risk functions utilizing componentwise (penalized) least squares estimates as baselearners for fitting various kinds of generalized linear and generalized additive models to potentially highdimensional data. We give a theoretical background and demonstrate how mboost can be used to fit interpretable models of different complexity. As an example we use mboost to predict the body fat based on anthropometric measurements throughout the tutorial. 1
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20130724
Source:
http://cran.rproject.org/web/packages/mboost/vignettes/mboost_tutorial.pdf
http://cran.rproject.org/web/packages/mboost/vignettes/mboost_tutorial.pdf
Minimize
Document Type:
text
Language:
en
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.303.8834
http://cran.rproject.org/web/packages/mboost/vignettes/mboost_tutorial.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.303.8834
http://cran.rproject.org/web/packages/mboost/vignettes/mboost_tutorial.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
7.
Variable Selection and Model Choice in Survival Models with TimeVarying Effects Boosting Survival Models
Open Access
Title:
Variable Selection and Model Choice in Survival Models with TimeVarying Effects Boosting Survival Models
Author:
Benjamin Hofner
;
Joint Work Thomas Kneib
;
Torsten Hothorn
Benjamin Hofner
;
Joint Work Thomas Kneib
;
Torsten Hothorn
Minimize authors
Description:
Cox PH model: λi (t) = λ(t, xi) = λ0(t) exp(x
Cox PH model: λi (t) = λ(t, xi) = λ0(t) exp(x
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20150305
Source:
http://www.statistik.unidortmund.de/useR2008/slides/
Hofner
+Kneib+Hothorn.pdf
http://www.statistik.unidortmund.de/useR2008/slides/
Hofner
+Kneib+Hothorn.pdf
Minimize
Document Type:
text
Language:
en
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.565.7961
http://www.statistik.unidortmund.de/useR2008/slides/Hofner+Kneib+Hothorn.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.565.7961
http://www.statistik.unidortmund.de/useR2008/slides/Hofner+Kneib+Hothorn.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
8.
Title ModelBased Boosting Version 2.011 Date 20110317
Open Access
Title:
Title ModelBased Boosting Version 2.011 Date 20110317
Author:
Torsten Hothorn
;
Peter Buehlmann
;
Thomas Kneib
;
Matthias Schmid
;
Benjamin Hofner
Torsten Hothorn
;
Peter Buehlmann
;
Thomas Kneib
;
Matthias Schmid
;
Benjamin Hofner
Minimize authors
Description:
Description Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing componentwise (penalised) least squares estimates or regression trees as baselearners for fitting generalized linear, additive and interaction models to potentially highdimensional data. Depends R (> = 2.9.0), methods, stats
Description Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing componentwise (penalised) least squares estimates or regression trees as baselearners for fitting generalized linear, additive and interaction models to potentially highdimensional data. Depends R (> = 2.9.0), methods, stats
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20120325
Source:
http://cran.rproject.org/web/packages/mboost/mboost.pdf
http://cran.rproject.org/web/packages/mboost/mboost.pdf
Minimize
Document Type:
text
Language:
en
Subjects:
Suggests multicore ; party (> = 0.99993 ; ipred ; MASS ; gbm LazyLoad yes LazyData yes
Suggests multicore ; party (> = 0.99993 ; ipred ; MASS ; gbm LazyLoad yes LazyData yes
Minimize
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.216.2110
http://cran.rproject.org/web/packages/mboost/mboost.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.216.2110
http://cran.rproject.org/web/packages/mboost/mboost.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
9.
opm: An R Package for Analysing Phenotype Microarray and Growth Curve Data
Open Access
Title:
opm: An R Package for Analysing Phenotype Microarray and Growth Curve Data
Author:
Johannes Sikorski
;
Lea A. I. Vaas
;
Benjamin Hofner
Johannes Sikorski
;
Lea A. I. Vaas
;
Benjamin Hofner
Minimize authors
Description:
The OmniLog ➤ Phenotype Microarray (PM) system is able to monitor simultaneously, on a longitudinal time scale, the phenotypic reaction of singlecelled organisms such as bacteria, fungi, and animal cell cultures to up to 2,000 environmental challenges spotted on sets of 96well microtiter plates. The phenotypic reactions are recorded as respi...
The OmniLog ➤ Phenotype Microarray (PM) system is able to monitor simultaneously, on a longitudinal time scale, the phenotypic reaction of singlecelled organisms such as bacteria, fungi, and animal cell cultures to up to 2,000 environmental challenges spotted on sets of 96well microtiter plates. The phenotypic reactions are recorded as respiration kinetics with a shape comparable to growth curves. Tools for storing the curve kinetics, aggregating the curve parameters, recording associated metadata of organisms and experimental settings as well as methods for analysing graphically and statistically these highly complex data sets are increasingly in demand. The opm R package facilitates management, visualisation and statistical analysis of PM data and similar data such as growth curves. Raw measurements can be easily input into R, combined with relevant metainformation and accordingly analysed. The kinetics can be aggregated by estimating curve parameters using several methods. Some of them have been specifically adapted for obtaining robust parameter estimates from PM data. Containers of opm data can easily be queried for and subset by using the integrated
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20141218
Source:
http://cran.at.rproject.org/web/packages/opm/vignettes/opmtutorial.pdf
http://cran.at.rproject.org/web/packages/opm/vignettes/opmtutorial.pdf
Minimize
Document Type:
text
Language:
en
Subjects:
Boo
Boo
Minimize
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.486.537
http://cran.at.rproject.org/web/packages/opm/vignettes/opmtutorial.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.486.537
http://cran.at.rproject.org/web/packages/opm/vignettes/opmtutorial.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
10.
Introduction Technical Preparations CoxflexBoost Summary / Outlook References
Open Access
Title:
Introduction Technical Preparations CoxflexBoost Summary / Outlook References
Author:
Benjamin Hofner
;
Thomas Kneib
;
Torsten Hothorn
Benjamin Hofner
;
Thomas Kneib
;
Torsten Hothorn
Minimize authors
Description:
Cox PH model: λi (t) = λ(t, xi) = λ0(t) exp(x
Cox PH model: λi (t) = λ(t, xi) = λ0(t) exp(x
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20141201
Source:
http://
benjamin
hofner
.de/downloads/2008/talks/DStatG_14052008_CoxFlexBoost.pdf
http://
benjamin
hofner
.de/downloads/2008/talks/DStatG_14052008_CoxFlexBoost.pdf
Minimize
Document Type:
text
Language:
en
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.460.6174
http://benjaminhofner.de/downloads/2008/talks/DStatG_14052008_CoxFlexBoost.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.460.6174
http://benjaminhofner.de/downloads/2008/talks/DStatG_14052008_CoxFlexBoost.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
Export Record
All Records
Export
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Adjust your hit list
Sort Your Results
Refine Search Result
More Options
Sort Your Results
Sort by:
Relevance
Author, ZA
Author, AZ
Title, AZ
Title, ZA
Date of publication, descending
Date of publication, ascending
Refine Search Result
Author
(21) Hofner, Benjamin
(21) The Pennsylvania State University CiteSeerX...
(18) Benjamin Hofner
(11) Thomas Kneib
(11) Torsten Hothorn
(10) Matthias Schmid
(8) Kneib, Thomas
(5) Hothorn, Torsten
(5) Mattthias Schmid
(4) Peter Buehlmann
(4) Schmid, Matthias
(3) Andreas Mayr
(3) Eth Zürich
(3) Fau Erlangennürnberg
(3) Graessel, Elmar
(3) Göker, Markus
(3) Johannes Sikorski
(3) Lmu München
(3) Luttenberger, Katharina
(3) Mayr, Andreas
(3) Peter Bühlmann
(3) Universität Oldenburg
(2) FrankRaue, Karin
(2) Gtari, Maher
(2) Haag, Christine
(2) Hartl, Wolfgang
(2) Hezbri, Karima
(2) Klenk, HansPeter
(2) Küchenhoff, Helmut
(2) Lea A. I
(2) Lea A. I. Vaas
(2) Letz, Saskia
(2) Mayr, Bernhard
(2) Needscompilation No
(2) Nikolay Robinzonov
(2) Raue, Friedhelm
(2) Rohde, Manfred
(2) Schulze, Egbert
(2) Schumann, Peter
(2) Schöfl, Christof
(2) Spröer, Cathrin
(1) Anne Fiebig
(1) Benjamin Hofner aut
(1) Boccuto, Luigi
(1) Buddruhs Anne Fiebig
(1) Contributions Benjamin Hofner
(1) Depends R
(1) Fabian Scheipl ctb
(1) Fabian Sobotka ctb
(1) Fenske, Nora
(1) Graessel Elmar
(1) Hanspeter Klenk
(1) Helmholtz Centre for infection research,...
(1) Hofner Benjamin
(1) Hofner Nora
(1) Joint Work Thomas Kneib
(1) Lawo, JohnPhilip
(1) Lazydata Yes
(1) Lazydatacompression Bzip
(1) Luttenberger Katharina
(1) Maintainer Benjamin Hofner
(1) Markus Goeker
(1) Markus Göker
(1) Matthias Schmid aut
(1) MonteroCalasanz, Maria Del Carmen
(1) MonteroCalasanz, Maria del Carmen
(1) Müller, Tina
(1) Nora Buddruhs
(1) Nora Fenske
(1) Peter Buehlmann aut
(1) Repsilber, Dirk
(1) Robinzonov, Nikolay
(1) Sikorski, Johannes
(1) Torsten Hothorn aut
(1) Vaas Benjamin
(1) Vaas, Lea
Author:
Subject
(7) splines
(5) ipred
(5) technische reports
(4) ddc 510
(4) party 0 9 9993
(3)
(3) component wise functional gradient descent
(3) decision trees
(3) imports matrix
(3) mass
(3) research article
(3) survival
(2) ddc 500
(2) ddc 610
(2) dementia
(2) follow up study
(2) gbm lazyload yes lazydata yes
(2) informatik und statistik
(2) lattice
(2) lattice suggests multicore
(2) mass lazyload yes lazydata yes
(2) model choice
(2) non drug therapy
(2) nursing home
(2) p splines
(2) rct
(2) statistics applications
(2) statistics computation
(2) suggests multicore
(1) 610 medicine health
(1) ausgewählte abschlussarbeiten
(1) bayesx
(1) biostatistics and prevention institute ebpi
(1) boo
(1) boosting
(1) boot
(1) bootstrap
(1) cas
(1) cell lines
(1) chebi
(1) ckmeans 1d dp
(1) doaj health sciences
(1) doaj medicine general
(1) doaj neurology
(1) epidemology
(1) fakultät für mathematik
(1) feature selection
(1) fields
(1) gbm
(1) gplots
(1) grofit
(1) grofit 1 1
(1) growth curves
(1) hazard regression
(1) hwriter
(1) imports methods
(1) kegg
(1) key words
(1) lattice suggests party 1 0 3
(1) lazyload yes
(1) lcc internal medicine
(1) lcc medicine
(1) lcc neurology diseases of the nervous system
(1) lcc neurosciences biological psychiatry...
(1) lcc r
(1) lcc rc31 1245
(1) lcc rc321 571
(1) lcc rc346 429
(1) likelihood based boosting
(1) mathematik
(1) mboost
(1) mesh
(1) metacyc
(1) metadat
(1) mgcv
(1) mlbench
(1) multcomp
(1) opmdata 0 4 0
(1) parallel
(1) pathview
(1) pathways
(1) pkgutils 0 3 0
(1) plotrix
(1) quantitative biology other quantitative biology
(1) rcolorbrewer
(1) respiration kinetics
(1) rjson 0 2 12 suggests optparse
(1) rpart 4 0 3 lazydata yes license gpl 2
(1) seed
(1) smooth effects
(1) statistics machine learning
(1) statistics methodology
(1) statistics other statistics
(1) statistik
(1) survival analysis
(1) testthat
(1) time varying effects
(1) vari able selection
(1) yaml 2 1 7
Subject:
Dewey Decimal Classification (DDC)
(8) Statistics [31*]
(3) Medicine & health [61*]
(2) Mathematics [51*]
(1) Computer science, knowledge & systems [00*]
(1) Library & information sciences [02*]
(1) Science [50*]
(1) Life sciences; biology [57*]
(1) Building & construction [69*]
Dewey Decimal Classification (DDC):
Year of Publication
(12) 2014
(8) 2012
(8) 2013
(5) 2011
(3) 2008
(3) 2010
(3) 2015
(1) 2009
Year of Publication:
Content Provider
(21) CiteSeerX
(6) Munich LMU: Open Access
(4) ArXiv.org
(3) PubMed Central
(3) ErlangenNuremberg Univ.: OPUS
(1) BioMed Central
(1) DataCite Metadata Store
(1) DOAJ Articles
(1) Helmholtz Centre of Infection Research (HZI)
(1) Munich LMU: Digital theses
(1) Zurich Univ.: ZORA
Content Provider:
Language
(36) English
(7) Unknown
Language:
Document Type
(28) Text
(7) Article, Journals
(5) Reports, Papers, Lectures
(2) Theses
(1) Unknown
Document Type:
Access
(33) Open Access
(10) Unknown
Access:
More Options
»
Search History
»
Get RSS Feed
»
Get ATOM Feed
»
Email this Search
»
Save Search
»
Browsing
»
Search Plugin
Further result pages
Results:
1

2

3

4

5
Next »
New Search »
Currently in BASE: 72,227,055 Documents of 3,466
Content Sources
About BASE

Contact

BASE Lab

Imprint
© 20042015 by
Bielefeld University Library
Search powered by
Solr
&
VuFind
.
Suggest Repository
BASE Interfaces
Currently in BASE: 72,227,055 Documents of 3,466 Content Sources
http://www.basesearch.net