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1.
Econometric Computing with HC and HAC Covariance Matrix Estimators
Open Access
Title:
Econometric Computing with HC and HAC Covariance Matrix Estimators
Author:
Achim Zeileis
Achim Zeileis
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Publisher:
University of California at Los Angeles, Department of Statistics
Year of Publication:
20040101T00:00:00Z
Document Type:
article
Language:
English
Subjects:
LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H
LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H
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2.
Objectoriented Computation of Sandwich Estimators
Open Access
Title:
Objectoriented Computation of Sandwich Estimators
Author:
Achim Zeileis
Achim Zeileis
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Description:
Sandwich covariance matrix estimators are a popular tool in applied regression modeling for performing inference that is robust to certain types of model misspecification. Suitable implementations are available in the R system for statistical computing for certain model fitting functions only (in particular lm()), but not for other standard regr...
Sandwich covariance matrix estimators are a popular tool in applied regression modeling for performing inference that is robust to certain types of model misspecification. Suitable implementations are available in the R system for statistical computing for certain model fitting functions only (in particular lm()), but not for other standard regression functions, such as glm(), nls(), or survreg(). Therefore, conceptual tools and their translation to computational tools in the package sandwich are discussed, enabling the computation of sandwich estimators in general parametric models. Object orientation can be achieved by providing a few extractor functionsmost importantly for the empirical estimating functionsfrom which various types of sandwich estimators can be computed.
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Publisher:
University of California at Los Angeles, Department of Statistics
Year of Publication:
20060801T00:00:00Z
Source:
Journal of Statistical Software, Vol 16, Iss 9 (2006)
Journal of Statistical Software, Vol 16, Iss 9 (2006)
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Document Type:
article
Language:
English
Subjects:
covariance matrix estimators ; estimating functions ; object orientation ; R ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Soci...
covariance matrix estimators ; estimating functions ; object orientation ; R ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H
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3.
Econometric Computing with HC and HAC Covariance Matrix Estimators
Title:
Econometric Computing with HC and HAC Covariance Matrix Estimators
Author:
Achim Zeileis
Achim Zeileis
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Description:
Data described by econometric models typically contains autocorrelation and/or heteroskedasticity of unknown form and for inference in such models it is essential to use covariance matrix estimators that can consistently estimate the covariance of the model parameters. Hence, suitable heteroskedasticity consistent (HC) and heteroskedasticity and...
Data described by econometric models typically contains autocorrelation and/or heteroskedasticity of unknown form and for inference in such models it is essential to use covariance matrix estimators that can consistently estimate the covariance of the model parameters. Hence, suitable heteroskedasticity consistent (HC) and heteroskedasticity and autocorrelation consistent (HAC) estimators have been receiving attention in the econometric literature over the last 20 years. To apply these estimators in practice, an implementation is needed that preferably translates the conceptual properties of the underlying theoretical frameworks into computational tools. In this paper, such an implementation in the package sandwich in the R system for statistical computing is described and it is shown how the suggested functions provide reusable components that build on readily existing functionality and how they can be integrated easily into new inferential procedures or applications. The toolbox contained in sandwich is extremely flexible and comprehensive, including specific functions for the most important HC and HAC estimators from the econometric literature. Several realworld data sets are used to illustrate how the functionality can be integrated into applications.
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http://www.jstatsoft.org/v11/i10/paper
http://www.jstatsoft.org/v11/i10/paper
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4.
Objectoriented Computation of Sandwich Estimators
Title:
Objectoriented Computation of Sandwich Estimators
Author:
Achim Zeileis
Achim Zeileis
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Description:
Sandwich covariance matrix estimators are a popular tool in applied regression modeling for performing inference that is robust to certain types of model misspecification. Suitable implementations are available in the R system for statistical computing for certain model fitting functions only (in particular lm()), but not for other standard regr...
Sandwich covariance matrix estimators are a popular tool in applied regression modeling for performing inference that is robust to certain types of model misspecification. Suitable implementations are available in the R system for statistical computing for certain model fitting functions only (in particular lm()), but not for other standard regression functions, such as glm(), nls(), or survreg(). Therefore, conceptual tools and their translation to computational tools in the package sandwich are discussed, enabling the computation of sandwich estimators in general parametric models. Object orientation can be achieved by providing a few extractor functions' most importantly for the empirical estimating functions' from which various types of sandwich estimators can be computed.
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http://www.jstatsoft.org/v16/i09/paper
http://www.jstatsoft.org/v16/i09/paper
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5.
Alternative boundaries for CUSUM tests
Title:
Alternative boundaries for CUSUM tests
Author:
Achim Zeileis
Achim Zeileis
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Description:
CUSUM test, structural change
CUSUM test, structural change
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Document Type:
article
URL:
http://hdl.handle.net/10.1007/BF02778274
http://hdl.handle.net/10.1007/BF02778274
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6.
A Unified Approach to Structural Change Tests Based on ML Scores, F Statistics, and OLS Residuals
Title:
A Unified Approach to Structural Change Tests Based on ML Scores, F Statistics, and OLS Residuals
Author:
Achim Zeileis
Achim Zeileis
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Description:
Three classes of structural change tests (or tests for parameter instability) that have been receiving much attention in both the statistics and the econometrics communities but have been developed in rather loosely connected lines of research are unified by embedding them into the framework of generalized Mfluctuation tests (
Zeileis
and Hornik...
Three classes of structural change tests (or tests for parameter instability) that have been receiving much attention in both the statistics and the econometrics communities but have been developed in rather loosely connected lines of research are unified by embedding them into the framework of generalized Mfluctuation tests (
Zeileis
and Hornik, 2003). These classes are tests based on maximum likelihood scores (including the NyblomHansen test), on F statistics (sup F, ave F, exp F tests), and on OLS residuals (OLSbased CUSUM and MOSUM tests). We show that (representatives from) these classes are special cases of the generalized Mfluctuation tests, based on the same functional central limit theorem but employing different functionals for capturing excessive fluctuations. After embedding these tests into the same framework and thus understanding the relationship between these procedures for testing in historical samples, it is shown how the tests can also be extended to a monitoring situation. This is achieved by establishing a general Mfluctuation monitoring procedure and then applying the different functionals corresponding to monitoring with ML scores, F statistics, and OLS residuals. In particular, an extension of the sup F test to a monitoring scenario is suggested and illustrated on a realworld data set. ; Aggregation functional, Fluctuation test, Functional central limit theorem, Monitoring, NyblomHansen test, OLSbased CUSUM test, Parameter instability, Structural change, sup F test
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http://www.tandfonline.com/doi/abs/10.1080/07474930500406053
http://www.tandfonline.com/doi/abs/10.1080/07474930500406053
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7.
Automatic Generation of Exams in R
Open Access
Title:
Automatic Generation of Exams in R
Author:
Bettina Grn
;
Achim Zeileis
Bettina Grn
;
Achim Zeileis
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Description:
Package exams provides a framework for automatic generation of standardized statistical exams which is especially useful for largescale exams. To employ the tools, users just need to supply a pool of exercises and a master file controlling the layout of the final PDF document. The exercises are specified in separate Sweave files (containing R c...
Package exams provides a framework for automatic generation of standardized statistical exams which is especially useful for largescale exams. To employ the tools, users just need to supply a pool of exercises and a master file controlling the layout of the final PDF document. The exercises are specified in separate Sweave files (containing R code for data generation and LaTeX code for problem and solution description) and the master file is a LaTeX document with some additional control commands. This paper gives an overview of the main design aims and principles as well as strategies for adaptation and extension. Handson illustrationsbased on example exercises and control files provided in the packageare presented to get new users started easily.
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Publisher:
University of California, Los Angeles
Year of Publication:
20090201T00:00:00Z
Document Type:
article
Language:
English
Subjects:
LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H
LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H
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8.
Extended Model Formulas in R: Multiple Parts and Multiple Responses
Open Access
Title:
Extended Model Formulas in R: Multiple Parts and Multiple Responses
Author:
Yves Croissant
;
Achim Zeileis
Yves Croissant
;
Achim Zeileis
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Description:
Model formulas are the standard approach for specifying the variables in statistical models in the S language. Although being eminently useful in an extremely wide class of applications, they have certain limitations including being confined to single responses and not providing convenient support for processing formulas with multiple parts. The...
Model formulas are the standard approach for specifying the variables in statistical models in the S language. Although being eminently useful in an extremely wide class of applications, they have certain limitations including being confined to single responses and not providing convenient support for processing formulas with multiple parts. The latter is relevant for models with two or more sets of variables, e.g., different equations for different model parameters (such as mean and dispersion), regressors and instruments in instrumental variable regressions, twopart models such as hurdle models, or alternativespecific and individualspecific variables in choice models among many others. The R package Formula addresses these two problems by providing a new class Formula (inheriting from formula) that accepts an additional formula operator  separating multiple parts and by allowing all formula operators (including the new ) on the lefthand side to support multiple responses.
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Publisher:
University of California, Los Angeles
Year of Publication:
20101001T00:00:00Z
Document Type:
article
Language:
English
Subjects:
formula processing ; model frame ; model matrix ; R. ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; LC...
formula processing ; model frame ; model matrix ; R. ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H
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9.
Econometrics in R: Past, Present, and Future
Open Access
Title:
Econometrics in R: Past, Present, and Future
Author:
Achim Zeileis
;
Roger Koenker
Achim Zeileis
;
Roger Koenker
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Description:
Recently, computational methods and software have been receiving more attention in the econometrics literature, emphasizing that they are integral components of modern econometric research. This has also promoted the development of many new econometrics software packages written in R and made available on the Comprehensive R Archive Network. Thi...
Recently, computational methods and software have been receiving more attention in the econometrics literature, emphasizing that they are integral components of modern econometric research. This has also promoted the development of many new econometrics software packages written in R and made available on the Comprehensive R Archive Network. This special volume on "Econometrics in R" features a selection of these recent activities that includes packages for econometric analysis of crosssection, time series and panel data. This introduction to the special volume highlights the contents of the contributions and embeds them into a brief overview of other past, present, and future projects for econometrics in R.
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Publisher:
University of California at Los Angeles, Department of Statistics
Year of Publication:
20080701T00:00:00Z
Document Type:
article
Language:
English
Subjects:
econometrics ; opensource software ; R ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics...
econometrics ; opensource software ; R ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H
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10.
zoo: S3 Infrastructure for Regular and Irregular Time Series
Open Access
Title:
zoo: S3 Infrastructure for Regular and Irregular Time Series
Author:
Gabor Grothendieck
;
Achim Zeileis
Gabor Grothendieck
;
Achim Zeileis
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Publisher:
University of California at Los Angeles, Department of Statistics
Year of Publication:
20050101T00:00:00Z
Document Type:
article
Language:
English
Subjects:
LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H
LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA14737 ; LCC:Social Sciences ; LCC:H
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Author
(219) Achim Zeileis
(206) The Pennsylvania State University CiteSeerX...
(146) Zeileis, Achim
(111) Kurt Hornik
(55) Torsten Hothorn
(52) Hornik, Kurt
(48) Friedrich Leisch
(42) Wirtschaftsuniversität Wien
(33) Christian Kleiber
(32) Universität Innsbruck
(31) Kleiber, Christian
(28) Achim Zeileis (eds
(24) Strobl, Carolin
(21) Carolin Strobl
(21) Hothorn, Torsten
(15) Leisch, Friedrich
(14) David Meyer
(13) Mark A. Van De Wiel
(13) On Distributed Statistical
(13) Universität München
(12) Gabor Grothendieck
(10) Ajay Shah
(10) Leitner, Christoph
(9) Bettina Grün
(9) Rusch, Thomas
(8) Achim Zeileis aut
(8) Boulesteix, AnneLaure
(8) Simon Jackman
(7) Ila Patnaik
(7) Lazydata Yes
(6) Contributions From Christian Buchta
(6) Kopf, Julia
(6) Lazyload Yes
(6) Yves Croissant
(5) Florian Wickelmaier
(5) Hannah Frick
(5) Ioannis Kosmidis
(5) Julia Kopf
(5) Lee, Ilro
(5) Mayr, Georg J.
(5) Nikolaus Umlauf
(5) Paul Murrell
(5) Verbesselt, Jan
(5) Wu Wirtschaftsuniversität
(4) Alexandros Karatzoglou
(4) Annelaure Boulesteix
(4) Frick, Hannah
(4) Jan Verbesselt
(4) Jank, Wolfgang
(4) Johannes Kepler
(4) Kneib, Thomas
(4) Krämer, Walter
(4) Merkle, Edgar C.
(4) Meyer, David
(4) Needscompilation No
(4) Needscompilation Yes
(4) Patnaik, Ila
(4) Shah, Ajay
(4) Universität Linz
(4) Wickelmaier, Florian
(3) Alex Smola
(3) Augustin, Thomas
(3) Bodenkultur Wien
(3) Edgar C. Merkle
(3) Francisco Cribarineto
(3) Georg J. Mayr
(3) In Cooperation
(3) Mark Van De Wiel
(3) Martin Herold
(3) Messner, Jakob W.
(3) Roger Koenker
(3) Technische Universität Wien
(3) Thomas Kneib
(3) Thomas Windberger
(3) Umlauf, Nikolaus
(3) Université De La Réunion
(3) Wu Wirtschaftsuniversität Wien
(2) Achim Zeileis ctb
(2) Angela Donini
(2) Anmol Sethy
(2) Annapaola Rizzoli
(2) Bettina Grn
(2) Boulesteix AnneLaure
(2) C. Chemini
(2) Cesare Furlanello
(2) Christian Buchta
(2) Christoph Leitner
(2) Darius Culvenor
(2) Eberhard Karls
(2) Francisco CribariNeto
(2) Georg Mayr
(2) Giovanni Millo
(2) Grun, Bettina
(2) Grün, Bettina
(2) Herold, Martin
(2) Jackman, Simon
(2) Jakob Messner
(2) Jakob W. Messner
(2) John Fox
(2) Karatzoglou, Alexandros
Author:
Subject
(86) r
(28) ddc 330
(27) structural change
(21) recursive partitioning
(16) doaj mathematics and statistics
(16) doaj statistics
(16) lcc h
(16) lcc ha1 4737
(16) lcc social sciences
(16) lcc statistics
(15) cusum
(15) mosum
(15) moving estimates
(13) c52
(13) recursive estimates
(13) s
(12) ddc 310
(11) beta regression
(10) conditional inference
(10) covariance matrix estimators
(10) estimating functions
(10) monitoring
(10) online monitoring
(9) c87
(9) hcl colors
(9) object orientation
(9) reproducibility
(8) irregular time series
(8) parameter instability
(8) regular time series
(8) s3
(8) technische reports
(8) totally ordered observations
(7) c22
(7) diverging palette
(7) finite mixture
(7) glm
(7) hurdle model
(7) qualitative palette
(7) sequential palette
(7) stats
(6) bias correction
(6) bias reduction
(6) breakpoints
(6) c53
(6) econometric software
(6) exams
(6) heteroskedasticity
(6) hsv colors
(6) independence
(6) mixed rasch model
(6) negative binomial model
(6) numerical accuracy
(6) open source software
(6) permutation tests
(6) poisson model
(6) theorie
(5) 3
(5) arithmetic problems
(5) association plots
(5) changepoint problem
(5) contingency tables
(5) exact distribution
(5) factor analysis
(5) flexmix
(5) formula processing
(5) graphics
(5) maximum likelihood
(5) measurement invariance
(5) model frame
(5) model matrix
(5) mosaic plots
(5) multiple choice
(5) pages 257 263 copyright © 2006 american...
(5) parameter stability
(5) proportions
(5) rates
(5) segmented regressions
(5) strukturbruch
(5) zero inflated model
(4) 150 psychology
(4) autocorrelation
(4) bookmakers odds
(4) business
(4) c14
(4) c25
(4) c38
(4) categorical data
(4) categorical data analysis
(4) classification trees
(4) conditional independence
(4) conditional monte carlo
(4) consensus
(4) gauss
(4) grid
(4) hcl
(4) hsv
(4) institute of psychology
(4) kernel methods
(4) latex
Subject:
Dewey Decimal Classification (DDC)
(97) Statistics [31*]
(35) Economics [33*]
(16) Mathematics [51*]
(10) Computer science, knowledge & systems [00*]
(3) Earth sciences & geology [55*]
(3) Sports, games & entertainment [79*]
(2) Library & information sciences [02*]
(2) Magazines, journals & serials [05*]
(2) Geography & travel [91*]
(1) Other religions [29*]
(1) Science [50*]
(1) Literature, rhetoric & criticism [80*]
(1) History [90*]
Dewey Decimal Classification (DDC):
Year of Publication
(67) 2013
(56) 2010
(41) 2009
(33) 2014
(32) 2008
(32) 2011
(30) 2012
(12) 2005
(12) 2006
(12) 2007
(12) 2015
(10) 2004
(8) 2002
(5) 2001
(4) 2003
(1) 2000
Year of Publication:
Content Provider
(206) CiteSeerX
(57) Vienna Univ. of Economics and Business: ePubWU
(40) RePEc.org
(31) EconStor
(19) DOAJ Articles
(10) Basel Univ.: edoc
(9) Munich LMU: Open Access
(6) Dortmund TU: Eldorado
(6) Zurich Univ.: ZORA
(4) Wollongong Univ.
(3) DataCite Metadata Store
(3) PubMed Central
(2) BioMed Central
(2) HighWire Press
(1) ArXiv.org
(1) Project Euclid
(1) Deakin Univ.: Deakin Research Online
(1) Hrčak (EJournals of Croatia)
(1) MDPI Open Access Publishing
(1) Monash Univ.: Research Repository
(1) Munich LMU: Munich Personal RePEc Archive (MPRA)
(1) Eindhoven Univ. of Technology: Repository TU/e
(1) Rome Tre Univ.: DSpace
Content Provider:
Language
(347) English
(59) Unknown
(1) German
Language:
Document Type
(222) Text
(91) Reports, Papers, Lectures
(89) Article, Journals
(4) Unknown
(1) Books
Document Type:
Access
(272) Open Access
(135) Unknown
Access:
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