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1.
FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R
Open Access
Title:
FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R
Author:
Friedrich Leisch
Friedrich Leisch
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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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http://www.jstatsoft.org/v11/i08/paper
URL:
http://doaj.org/search?source=%7B%22query%22%3A%7B%22bool%22%3A%7B%22must%22%3A%5B%7B%22term%22%3...
http://doaj.org/search?source=%7B%22query%22%3A%7B%22bool%22%3A%7B%22must%22%3A%5B%7B%22term%22%3...
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2.
FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R
Title:
FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R
Author:
Friedrich Leisch
Friedrich Leisch
Minimize authors
Description:
FlexMix implements a general framework for fitting discrete mixtures of regression models in the R statistical computing environment: three variants of the EM algorithm can be used for parameter estimation, regressors and responses may be multivariate with arbitrary dimension, data may be grouped, e.g., to account for multiple observations per i...
FlexMix implements a general framework for fitting discrete mixtures of regression models in the R statistical computing environment: three variants of the EM algorithm can be used for parameter estimation, regressors and responses may be multivariate with arbitrary dimension, data may be grouped, e.g., to account for multiple observations per individual, the usual formula interface of the S language is used for convenient model specification, and a modular concept of driver functions allows to interface many different types of regression models. Existing drivers implement mixtures of standard linear models, generalized linear models and modelbased clustering. FlexMix provides the Estep and all data handling, while the Mstep can be supplied by the user to easily define new models.
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Document Type:
article
URL:
http://www.jstatsoft.org/v11/i08/paper
http://www.jstatsoft.org/v11/i08/paper
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RePEc: Research Papers in Economics
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3.
FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters
Open Access
Title:
FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters
Author:
Bettina Grun
;
Friedrich Leisch
Bettina Grun
;
Friedrich Leisch
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Description:
flexmix provides infrastructure for flexible fitting of finite mixture models in R using the expectationmaximization (EM) algorithm or one of its variants. The functionality of the package was enhanced. Now concomitant variable models as well as varying and constant parameters for the component specific generalized linear regression models can ...
flexmix provides infrastructure for flexible fitting of finite mixture models in R using the expectationmaximization (EM) algorithm or one of its variants. The functionality of the package was enhanced. Now concomitant variable models as well as varying and constant parameters for the component specific generalized linear regression models can be fitted. The application of the package is demonstrated on several examples, the implementation described and examples given to illustrate how new drivers for the component specific models and the concomitant variable models can be defined.
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Publisher:
University of California, Los Angeles
Year of Publication:
20080901T00:00:00Z
Source:
Journal of Statistical Software, Vol 28, Iss 4 (2008)
Journal of Statistical Software, Vol 28, Iss 4 (2008)
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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...
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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DDC:
310 Collections of general statistics
(computed)
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CC BY
CC BY
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http://www.jstatsoft.org/v28/i04/paper
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http://doaj.org/search?source=%7B%22query%22%3A%7B%22bool%22%3A%7B%22must%22%3A%5B%7B%22term%22%3...
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4.
Identifiability of Finite Mixtures of Multinomial Logit Models with Varying and Fixed Effects
Title:
Identifiability of Finite Mixtures of Multinomial Logit Models with Varying and Fixed Effects
Author:
Bettina Grün
;
Friedrich Leisch
Bettina Grün
;
Friedrich Leisch
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Description:
Conditional logit, Finite mixture, Identifiability, Multinomial logit, Unobserved heterogeneity
Conditional logit, Finite mixture, Identifiability, Multinomial logit, Unobserved heterogeneity
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Document Type:
article
URL:
http://hdl.handle.net/10.1007/s0035700890228
http://hdl.handle.net/10.1007/s0035700890228
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RePEc: Research Papers in Economics
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5.
FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters
Title:
FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters
Author:
Friedrich Leisch
;
Bettina Grün
Friedrich Leisch
;
Bettina Grün
Minimize authors
Description:
flexmix provides infrastructure for flexible fitting of finite mixture models in R using the expectationmaximization (EM) algorithm or one of its variants. The functionality of the package was enhanced. Now concomitant variable models as well as varying and constant parameters for the component specific generalized linear regression models can ...
flexmix provides infrastructure for flexible fitting of finite mixture models in R using the expectationmaximization (EM) algorithm or one of its variants. The functionality of the package was enhanced. Now concomitant variable models as well as varying and constant parameters for the component specific generalized linear regression models can be fitted. The application of the package is demonstrated on several examples, the implementation described and examples given to illustrate how new drivers for the component specific models and the concomitant variable models can be defined.
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Document Type:
article
URL:
http://www.jstatsoft.org/v28/i04/paper
http://www.jstatsoft.org/v28/i04/paper
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RePEc: Research Papers in Economics
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6.
R Version 2.0.0
Title:
R Version 2.0.0
Author:
Kurt Hornik
;
Friedrich Leisch
Kurt Hornik
;
Friedrich Leisch
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Document Type:
article
URL:
http://hdl.handle.net/10.1007/BF02753918
http://hdl.handle.net/10.1007/BF02753918
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RePEc: Research Papers in Economics
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7.
Exploratory analysis of benchmark experiments an interactive approach
Title:
Exploratory analysis of benchmark experiments an interactive approach
Author:
Manuel Eugster
;
Friedrich Leisch
Manuel Eugster
;
Friedrich Leisch
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Description:
Benchmark experiment, Visualization, Interactive data analysis
Benchmark experiment, Visualization, Interactive data analysis
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Document Type:
article
URL:
http://hdl.handle.net/10.1007/s001800100227z
http://hdl.handle.net/10.1007/s001800100227z
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8.
Evaluation of structure and reproducibility of cluster solutions using the bootstrap
Title:
Evaluation of structure and reproducibility of cluster solutions using the bootstrap
Author:
Sara Dolnicar
;
Friedrich Leisch
Sara Dolnicar
;
Friedrich Leisch
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Description:
Cluster analysis, Mixture models, Bootstrap
Cluster analysis, Mixture models, Bootstrap
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Document Type:
article
URL:
http://hdl.handle.net/10.1007/s1100200990834
http://hdl.handle.net/10.1007/s1100200990834
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9.
Sweave: Dynamic generation of statistical reports using literate data analysis
Open Access
Title:
Sweave: Dynamic generation of statistical reports using literate data analysis
Author:
Friedrich Leisch
;
Friedrich Leisch
Friedrich Leisch
;
Friedrich Leisch
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Description:
This paper has been accepted for publication in:
This paper has been accepted for publication in:
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Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20141212
Source:
http://epub.wu.ac.at/1788/1/document.pdf
http://epub.wu.ac.at/1788/1/document.pdf
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Document Type:
text
Language:
en
Subjects:
S ; literate statistical practice ; integrated statistical documents ; reproducible research
S ; literate statistical practice ; integrated statistical documents ; reproducible research
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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.
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.475.286
http://epub.wu.ac.at/1788/1/document.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.475.286
http://epub.wu.ac.at/1788/1/document.pdf
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10.
Bagged Clustering
Open Access
Title:
Bagged Clustering
Author:
Friedrich Leisch
;
Friedrich Leisch
Friedrich Leisch
;
Friedrich Leisch
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Description:
 A new ensemble method for cluster analysis is introduced, which can be interpreted in two dierent ways: As complexityreducing preprocessing stage for hierarchical clustering and as combination procedure for several partitioning results. The basic idea is to locate and combine structurally stable cluster centers and/or prototypes. Random eects...
 A new ensemble method for cluster analysis is introduced, which can be interpreted in two dierent ways: As complexityreducing preprocessing stage for hierarchical clustering and as combination procedure for several partitioning results. The basic idea is to locate and combine structurally stable cluster centers and/or prototypes. Random eects of the training set are reduced by repeatedly training on resampled sets (bootstrap samples). We discuss the algorithm both from a more theoretical and an applied point of view and demonstrate it on several data sets. Keywords cluster analysis, bagging, bootstrap samples, kmeans, learning vector quantization I. Introduction Clustering is an old data analysis problem and numerous methods have been developed to solve this task. Most of the currently popular clustering techniques fall into one of the following two major categories: Partitioning Methods Hierarchical Methods Both methods have in common that they try to group the data s.
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Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090415
Source:
http://www.ci.tuwien.ac.at/~
leisch
/docs/papers/wp51.ps.gz
http://www.ci.tuwien.ac.at/~
leisch
/docs/papers/wp51.ps.gz
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Document Type:
text
Language:
en
Subjects:
bagging ; bootstrap samples ; kmeans ; learning vector quantization
bagging ; bootstrap samples ; kmeans ; learning vector quantization
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DDC:
004 Data processing & computer science
(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.
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.642
http://www.ci.tuwien.ac.at/~leisch/docs/papers/wp51.ps.gz
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.642
http://www.ci.tuwien.ac.at/~leisch/docs/papers/wp51.ps.gz
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(185) Friedrich Leisch
(174) The Pennsylvania State University CiteSeerX...
(171) Leisch, Friedrich
(69) Kurt Hornik
(40) Dolnicar, Sara
(30) Hornik, Kurt
(28) Achim Zeileis (eds
(25) Scharl, Theresa
(23) Bettina Grün
(21) Achim Zeileis
(20) Ludwigmaximiliansuniversität München
(18) Sara Dolnicar
(14) Grun, Bettina
(14) Hothorn, Torsten
(14) Zeileis, Achim
(13) Christian Kleiber
(13) On Distributed Statistical
(12) Theresa Scharl
(10) Manuel J. A. Eugster
(9) Andreas Weingessel
(9) Grün, Bettina
(9) In Cooperation
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(9) Kleiber, Christian
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(8) Eugster, Manuel J. A.
(8) Wirtschaftsuniversität Wien
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(6) Evgenia Dimitriadou
(6) Vierlinger, Klemens
(6) Weingessel, Andreas
(5) Adrian Trapletti
(5) Aschauer, Harald N
(5) Dettling, Marcel
(5) Dudoit, Sandrine
(5) Ellis, Byron
(5) Gautier, Laurent
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(5) Huber, Wolfgang
(5) Iacus, Stefano
(5) Irizarry, Rafael
(5) Kasper, Siegfried
(5) Lazyload Yes
(5) Li, Cheng
(5) Maechler, Martin
(5) Maintainer Friedrich Leisch
(5) Meyer, David
(5) Sieghart, Werner
(5) Smith, Colin
(5) Strobl, Carolin
(5) Technische Universität Wien
(5) Tierney, Luke
(5) Torsten Hothorn
(5) Zhang, Jianhua
(4) Alexandros Karatzoglou
(4) Bailer, Ursula
(4) Buchta, Christian
(4) Deeley, Jane
(4) Ellis, Shane
(4) Eugster, Manuel
(4) Friedrich Leisch aut
(4) Ingo Voglhuber
(4) Kindler, Jochen
(4) Leisch Friedrich
(4) Management Science
(4) Needscompilation No
(4) Schosser, Alexandra
(4) Trapletti, Adrian
(4) Universität Für Bodenkultur Wien
(4) Voglhuber, Ingo
(3) Bayer, Karl
(3) Bettina Grun
(3) Binder, Harald
(3) Bodenkultur Wien
(3) Bolstad, Ben
(3) Carey, Vincent J.
(3) Carolin Strobl
(3) Dilaveroglu, Erkan
(3) Fuchs, K
(3) Gentleman, Robert C.
(3) Hannah Frick
(3) Kaserer, Klaus
(3) Kestler, Hans A.
(3) Koperek, Oskar
(3) Kriegner, Albert
(3) Lakhmi C. Jain
(3) Lauss, Martin
(3) Lazarevski, Katie
(3) Mansfeld, Markus H
(3) Moeller, Teresa
(3) Noehammer, Christa
(3) Nöhammer, Christa
(3) Robert C Gentleman; Vincent J Carey; Douglas M...
(3) Rossini, Anthony J.
(3) Rossiter, John
(3) Sara Dolničar
(3) Sawitzki, Gunther
Author:
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(60) business
(56) social and behavioral sciences
(39) r
(20) technische reports
(15) cusum
(15) mosum
(15) moving estimates
(13) recursive estimates
(12) analysis
(12) s
(11) for
(10) finite
(10) models
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(10) structural change
(9) cluster
(9) finite mixture models
(9) market segmentation
(8) cluster analysis
(8) data
(8) gene
(8) stats
(7) archetypal analysis
(7) bootstrap
(7) convex hull
(7) mixtures
(7) reproducible research
(6) ddc 310
(6) flexmix
(6) monitoring
(5) binary
(5) c52
(5) ddc 510
(5) doaj mathematics and statistics
(5) doaj statistics
(5) graphs
(5) lcc h
(5) lcc ha1 4737
(5) lcc social sciences
(5) lcc statistics
(5) linear
(5) marktsegmentierung
(5) regression
(5) segmentation
(4) bagged clustering
(4) beliefs
(4) binärdaten
(4) c22
(4) concomitant variables
(4) dimensionality
(4) doaj biology
(4) doaj biology and life sciences
(4) environmentally friendly tourists
(4) evaluative
(4) generalized
(4) generalized linear models
(4) integrated statistical documents
(4) latent class regression
(4) lcc q
(4) lcc science
(4) legacy
(4) literate statistical practice
(4) market
(4) mazanec
(4) measure
(4) mixture
(4) mlbench
(4) model based clustering
(4) neighborhood
(4) one
(4) overcoming
(4) simple
(4) stable
(4) statistik
(4) sustainable tourism
(4) valid
(3) benchmark
(3) bioinformatics
(3) cluster analyse
(3) clusters
(3) computational biology
(3) cross validation
(3) ddc 330
(3) effects
(3) era2010
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(3) fremdenverkehr
(3) gene expression
(3) hypothesis testing
(3) interactive
(3) lcc biology general
(3) lcc qh301 705 5
(3) medicine and health sciences
(3) mixed rasch model
(3) mixture model
(3) model comparison
(3) modelling
(3) multi category
(3) ordinal
(3) package
Subject:
Dewey Decimal Classification (DDC)
(49) Statistics [31*]
(34) Computer science, knowledge & systems [00*]
(9) Geography & travel [91*]
(4) Economics [33*]
(2) Library & information sciences [02*]
(2) Mathematics [51*]
(2) Life sciences; biology [57*]
(1) Magazines, journals & serials [05*]
(1) Psychology [15*]
(1) Religion [20*]
(1) Social sciences, sociology & anthropology...
(1) Commerce, communications & transportation...
(1) Music [78*]
Dewey Decimal Classification (DDC):
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(83) 2009
(46) 2010
(43) 2011
(39) 2008
(37) 2013
(24) 2012
(15) 2014
(10) 2002
(10) 2004
(9) 2003
(8) 2005
(7) 1998
(7) 2007
(5) 2001
(4) 1999
(3) 2015
(2) 2000
(1) 2006
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(80) Wollongong Univ.
(24) Vienna Univ. of Economics and Business: ePubWU
(20) Munich LMU: Open Access
(16) RePEc.org
(9) DOAJ Articles
(8) PubMed Central
(5) BioMed Central
(5) HighWire Press
(5) Queensland Univ.: UQ eSpace
(4) Collection of Biostatistics Research Archiv
(4) EconStor
(3) DataCite Metadata Store
(3) Ulm Univ.: Institutional Repository
(2) Basel Univ.: edoc
(2) Bern Univ.: BORIS
(2) Dortmund TU: Eldorado
(1) Hrčak (EJournals of Croatia)
(1) Inst. de la Montagne
(1) Zurich Univ.: ZORA
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(261) English
(107) Unknown
(1) German
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(91) Article, Journals
(60) Reports, Papers, Lectures
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