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
227 hits
in 72,227,055 documents
in 0.34 seconds
Please leave the following field blank:
Home
»
Search: A. E. Raftery
Hit List
Hit list
1.
Modeling outliers, bursts and flat stretches in time series using Mixture Transition Distribution (MTD) models
Open Access
Title:
Modeling outliers, bursts and flat stretches in time series using Mixture Transition Distribution (MTD) models
Author:
R. D. Martin
;
A. E. Raftery
;
R. D. Martin
;
A. E. Raftery
R. D. Martin
;
A. E. Raftery
;
R. D. Martin
;
A. E. Raftery
Minimize authors
Description:
The class of Mixture Transition Distribution (MTD) time series models is introduced. In these models, the conditional distribution of the current observation given the past is a mixture of conditional distributions given each one of the last p observations. They can capture nonGaussian and nonlinear features such as outliers, bursts of activit...
The class of Mixture Transition Distribution (MTD) time series models is introduced. In these models, the conditional distribution of the current observation given the past is a mixture of conditional distributions given each one of the last p observations. They can capture nonGaussian and nonlinear features such as outliers, bursts of activity and flat stretches, in a single unified model class. They can also represent time series defined on arbitrary state spaces, which need not even be Euclidean. They perform well in the usual case of Gaussian time series without obvious nonstandard behaviors. The models are simple, analytically tractable, easy to simulate and readily estimated. The stationarity and autocorrelation properties of the models are derived. _A. simple EM algorithm is given and shown to work well for estimation. The models are applied to several real and simulated data sets with satisfactory results. They appear to capture the features of the data better than the best competing ARIMA models.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090806
Source:
http://www.stat.washington.edu/research/reports/1990/tr194.pdf
http://www.stat.washington.edu/research/reports/1990/tr194.pdf
Minimize
Document Type:
text
Language:
en
DDC:
519 Probabilities & applied mathematics
(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=10.1.1.143.1187
http://www.stat.washington.edu/research/reports/1990/tr194.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.1187
http://www.stat.washington.edu/research/reports/1990/tr194.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.
References Open Access Rapid responses
Open Access
Title:
References Open Access Rapid responses
Author:
L Alkema
;
A E Raftery
;
T Brown
;
Sex Transm Inf Ii
;
Email Alerting
;
L Alkema
;
A E Raftery
;
T Brown
L Alkema
;
A E Raftery
;
T Brown
;
Sex Transm Inf Ii
;
Email Alerting
;
L Alkema
;
A E Raftery
;
T Brown
Minimize authors
Description:
Bayesian melding for estimating uncertainty in
Bayesian melding for estimating uncertainty in
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20130717
Source:
http://www.stat.washington.edu/people/
raftery
/Research/PDF/AlkemaEtal2008STI.pdf
http://www.stat.washington.edu/people/
raftery
/Research/PDF/AlkemaEtal2008STI.pdf
Minimize
Document Type:
text
Language:
en
Subjects:
Statistics and the Social
Statistics and the Social
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=10.1.1.296.6676
http://www.stat.washington.edu/people/raftery/Research/PDF/AlkemaEtal2008STI.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.296.6676
http://www.stat.washington.edu/people/raftery/Research/PDF/AlkemaEtal2008STI.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.
MCLUST: Software for ModelBased Cluster Analysis
Open Access
Title:
MCLUST: Software for ModelBased Cluster Analysis
Author:
C. Fraley
;
A. E. Raftery
C. Fraley
;
A. E. Raftery
Minimize authors
Description:
ents the data, and k is an integer subscript specifying a particular cluster. Clusters are ellipsoidal, centered at the means ¯ k . The covariances \Sigma k determine their other geometric features. Each covariance matrix is parameterized by eigenvalue decomposition in the form \Sigma k = k D k A k D T k ; Funded by the Office of Naval Research ...
ents the data, and k is an integer subscript specifying a particular cluster. Clusters are ellipsoidal, centered at the means ¯ k . The covariances \Sigma k determine their other geometric features. Each covariance matrix is parameterized by eigenvalue decomposition in the form \Sigma k = k D k A k D T k ; Funded by the Office of Naval Research under contracts N000149610192 and N000149610330. 1 MathSoft, Inc., Seattle, WA USA  http://www.mathsoft.com/splus where D k is the orthogonal matrix of eigenvectors, A k is a diagonal matrix whose elements are proportional to the eigenvalues of \Sigma k , and k is a scalar. The orientation of the principal components of \Sigma k is deter
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090411
Source:
http://www.stat.washington.edu/tech.reports/tr342.ps
http://www.stat.washington.edu/tech.reports/tr342.ps
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=10.1.1.52.6959
http://www.stat.washington.edu/tech.reports/tr342.ps
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.6959
http://www.stat.washington.edu/tech.reports/tr342.ps
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.
biocViews Microarray, TwoChannel, DifferentialExpression Imports stats
Open Access
Title:
biocViews Microarray, TwoChannel, DifferentialExpression Imports stats
Author:
N. Dean
;
A. E. Raftery
;
Maintainer N. Dean
N. Dean
;
A. E. Raftery
;
Maintainer N. Dean
Minimize authors
Description:
Description Package for normalizing microarray data in single and multiple replicate experiments and fitting a normaluniform mixture to detect differentially expressed genes in the cases where the two samples are being compared directly or indirectly (via a common reference sample)
Description Package for normalizing microarray data in single and multiple replicate experiments and fitting a normaluniform mixture to detect differentially expressed genes in the cases where the two samples are being compared directly or indirectly (via a common reference sample)
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20130731
Source:
http://www.bioconductor.org/packages/2.12/bioc/manuals/nudge/man/nudge.pdf
http://www.bioconductor.org/packages/2.12/bioc/manuals/nudge/man/nudge.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=10.1.1.308.43
http://www.bioconductor.org/packages/2.12/bioc/manuals/nudge/man/nudge.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.43
http://www.bioconductor.org/packages/2.12/bioc/manuals/nudge/man/nudge.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.
How many clusters? Which clustering method? Answers via modelbased cluster analysis
Open Access
Title:
How many clusters? Which clustering method? Answers via modelbased cluster analysis
Author:
C. Fraley
;
A. E. Raftery
C. Fraley
;
A. E. Raftery
Minimize authors
Description:
0330. Thanks go to Simon Byers for providing the NNclean denoising procedure. We consider the problem of determining the structure of clustered data, without prior knowledge of the number of clusters or any other information about their composition. Data are represented by a mixture model in which each component corresponds to a different cluste...
0330. Thanks go to Simon Byers for providing the NNclean denoising procedure. We consider the problem of determining the structure of clustered data, without prior knowledge of the number of clusters or any other information about their composition. Data are represented by a mixture model in which each component corresponds to a different cluster. Models with varying geometric properties are obtained through Gaussian components with different parameterizations and crosscluster constraints. Noise and outliers can be modeled by adding a Poisson process component. Partitions are determined by the EM (expectationmaximization) algorithm for maximum likelihood, with initial values from agglomerative hierarchical clustering. Models are compared using an approximation to the Bayes factor based on the Bayesian Information Criterion (BIC); unlike significance tests, this allows comparison of more than two models at the same time, and removes the restriction that the models compared be nested. The problems of determining the number of clusters and the clustering method are solved simultaneously by choosing the best model. Moreover, the EM result provides a
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090106
Source:
http://www.ics.uci.edu/~smyth/courses/ics278/papers/fraley_clustering.pdf
http://www.ics.uci.edu/~smyth/courses/ics278/papers/fraley_clustering.pdf
Minimize
Document Type:
text
Language:
en
Subjects:
Contents
Contents
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=10.1.1.129.9139
http://www.ics.uci.edu/~smyth/courses/ics278/papers/fraley_clustering.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.129.9139
http://www.ics.uci.edu/~smyth/courses/ics278/papers/fraley_clustering.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.
How Many Clusters? Which Clustering Method? Answers Via ModelBased Cluster Analysis
Open Access
Title:
How Many Clusters? Which Clustering Method? Answers Via ModelBased Cluster Analysis
Author:
C. Fraley
;
A. E. Raftery
C. Fraley
;
A. E. Raftery
Minimize authors
Description:
We consider the problem of determining the structure of clustered data, without prior knowledge of the number of clusters or any other information about their composition. In modelbased cluster analysis, data are represented by a mixture model in which each component corresponds to a different cluster. Models with varying geometric properties a...
We consider the problem of determining the structure of clustered data, without prior knowledge of the number of clusters or any other information about their composition. In modelbased cluster analysis, data are represented by a mixture model in which each component corresponds to a different cluster. Models with varying geometric properties are obtained through Gaussian components with different parameterizations and crosscluster constraints. Noise can be modeled by adding a Poisson component. Partitions are determined by the EM (expectationmaximization) algorithm for maximum likelihood, with initial values from agglomerative hierarchical clustering. Models are compared using an approximation to the Bayes factor based on the Bayesian Information Criterion (BIC); unlike significance tests, this allows comparison of more than two models at the same time, and removes the restriction that the models compared be nested. The problems of determining the number of clusters and the cluster.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090411
Source:
http://www.ece.northwestern.edu/~harsha/Clustering/howMany.ps
http://www.ece.northwestern.edu/~harsha/Clustering/howMany.ps
Minimize
Document Type:
text
Language:
en
Subjects:
Contents
Contents
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=10.1.1.7.8739
http://www.ece.northwestern.edu/~harsha/Clustering/howMany.ps
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.7.8739
http://www.ece.northwestern.edu/~harsha/Clustering/howMany.ps
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.
Choosing the Link Function and Accounting for Link Uncertainty in Generalized Linear Models using Bayes Factors
Open Access
Title:
Choosing the Link Function and Accounting for Link Uncertainty in Generalized Linear Models using Bayes Factors
Author:
C. Czado
;
A. E. Raftery
C. Czado
;
A. E. Raftery
Minimize authors
Description:
this paper, we extend the approach taken by
Raftery
(1996) to calculate approximate Bayes factors for GLM's with a parametric link function. Even though GLM's with canonical links (for de nition see McCullagh and Nelder (1989)), such as the logit link in binomial regression, guarantee maximum information and a simple interpretation of the regres...
this paper, we extend the approach taken by
Raftery
(1996) to calculate approximate Bayes factors for GLM's with a parametric link function. Even though GLM's with canonical links (for de nition see McCullagh and Nelder (1989)), such as the logit link in binomial regression, guarantee maximum information and a simple interpretation of the regression parameters, they do not always provide the best t available to a given data set. Link misspeci  cation can lead to substantial bias in the regression parameters and the mean response estimates (see Czado and Santner (1992) for binomial responses). One common approach to guard against link misspeci cation in generalized linear models is to embed the canonical link in a wide parametric class of links = = fF (; ); 2 g, which includes the canonical link as a special case when = 0 . Many such parametric link classes for binary regression data have been proposed in the literature. Montfort and Otten (1976), Copenhaver and Mielke (1977), ArandaOrdaz (1981) , Guerrero and Johnson (1982), Morgan (1983) and Whittmore (1983) proposed oneparameter families, while Prentice (1976), Pregibon (1980), Stukel (1988) and Czado (1992) considered twoparameter families. Link functions for the nonbinary case were studied by Pregibon (1980) and Czado (1992, 1997)
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090417
Source:
http://wwwm4.mathematik.tumuenchen.de/m4/Papers/Czado/bayes.ps
http://wwwm4.mathematik.tumuenchen.de/m4/Papers/Czado/bayes.ps
Minimize
Document Type:
text
Language:
en
Subjects:
Key words ; Bayes factors ; link function ; GLM ; model selection ; reference
Key words ; Bayes factors ; link function ; GLM ; model selection ; reference
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=10.1.1.57.7415
http://wwwm4.mathematik.tumuenchen.de/m4/Papers/Czado/bayes.ps
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.57.7415
http://wwwm4.mathematik.tumuenchen.de/m4/Papers/Czado/bayes.ps
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.
How many clusters? Which clustering method? Answers via modelbased cluster analysis
Open Access
Title:
How many clusters? Which clustering method? Answers via modelbased cluster analysis
Author:
C. Fraley
;
A. E. Raftery
C. Fraley
;
A. E. Raftery
Minimize authors
Description:
We consider the problem of determining the structure of clustered data, without prior knowledge of the number of clusters or any other information about their composition. Data are represented by a mixture model in which each component corresponds to a different cluster. Models with varying geometric properties are obtained through Gaussian comp...
We consider the problem of determining the structure of clustered data, without prior knowledge of the number of clusters or any other information about their composition. Data are represented by a mixture model in which each component corresponds to a different cluster. Models with varying geometric properties are obtained through Gaussian components with different parameterizations and crosscluster constraints. Noise and outliers can be modeled by adding a Poisson process component. Partitions are determined by the EM (expectationmaximization) algorithm for maximum likelihood, with initial values from agglomerative hierarchical clustering. Models are compared using an approximation to the Bayes factor based on the Bayesian Information Criterion (BIC); unlike significance tests, this allows comparison of more than two models at the same time, and removes the restriction that the models compared be nested. The problems of determining the number of clusters and the clustering method are solved simultaneously by choosing the best model. Moreover, the EM result provides a measure of uncertainty about the associated classification of each data point.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20111029
Source:
http://www.albany.edu/~jz7088/documents/03182004/fraley.clustrng.no.pdf
http://www.albany.edu/~jz7088/documents/03182004/fraley.clustrng.no.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=10.1.1.199.4908
http://www.albany.edu/~jz7088/documents/03182004/fraley.clustrng.no.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.199.4908
http://www.albany.edu/~jz7088/documents/03182004/fraley.clustrng.no.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.
biocViews Microarray, TwoChannel, DifferentialExpression Imports stats
Open Access
Title:
biocViews Microarray, TwoChannel, DifferentialExpression Imports stats
Author:
N. Dean
;
A. E. Raftery
;
Maintainer N. Dean
N. Dean
;
A. E. Raftery
;
Maintainer N. Dean
Minimize authors
Description:
Description Package for normalizing microarray data in single and multiple replicate experiments and fitting a normaluniform mixture to detect differentially expressed genes in the cases where the two samples are being compared directly or indirectly (via a common reference sample)
Description Package for normalizing microarray data in single and multiple replicate experiments and fitting a normaluniform mixture to detect differentially expressed genes in the cases where the two samples are being compared directly or indirectly (via a common reference sample)
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20131015
Source:
http://www.bioconductor.org/packages/2.13/bioc/manuals/nudge/man/nudge.pdf
http://www.bioconductor.org/packages/2.13/bioc/manuals/nudge/man/nudge.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=10.1.1.366.9561
http://www.bioconductor.org/packages/2.13/bioc/manuals/nudge/man/nudge.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.366.9561
http://www.bioconductor.org/packages/2.13/bioc/manuals/nudge/man/nudge.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.
Linear Flaw Detection in Woven Textiles using ModelBased Clustering
Open Access
Title:
Linear Flaw Detection in Woven Textiles using ModelBased Clustering
Author:
J. G. Campbell
;
C. Fraley
;
F. Murtagh
;
A. E. Raftery
J. G. Campbell
;
C. Fraley
;
F. Murtagh
;
A. E. Raftery
Minimize authors
Description:
We combine imageprocessing techniques with a powerful new statistical technique to detect linear pattern production faults in woven textiles. Our approach detects a linear pattern in preprocessed images via modelbased clustering. It employs an approximate Bayes factor which provides a criterion for assessing the evidence for the presence of a ...
We combine imageprocessing techniques with a powerful new statistical technique to detect linear pattern production faults in woven textiles. Our approach detects a linear pattern in preprocessed images via modelbased clustering. It employs an approximate Bayes factor which provides a criterion for assessing the evidence for the presence of a defect. The model used in experimentation is a (possibly highly elliptical) Gaussian cloud superimposed on Poisson clutter. Results are shown for some representative examples, and contrasted with a Hough transform. Software for the statistical modeling is available. 1 Corresponding author. Keywords Modelbased clustering, pattern recognition, Bayesian cluster analysis, machine vision, industrial inspection, Hough transform. 1 The Flaw Detection Problem Garment production can be divided into two distinct phases: manufacture of the textile fabric, followed by garment assembly. The two phases are often performed in different locations and by d.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090412
Source:
http://www.infm.ulst.ac.uk/research/preprints/prl29apr97.ps.gz
http://www.infm.ulst.ac.uk/research/preprints/prl29apr97.ps.gz
Minimize
Document Type:
text
Language:
en
Subjects:
pattern recognition ; Bayesian cluster analysis ; machine vision ; industrial inspection ; Hough transform. 1 The Flaw Detection Problem
pattern recognition ; Bayesian cluster analysis ; machine vision ; industrial inspection ; Hough transform. 1 The Flaw Detection Problem
Minimize
DDC:
006 Special computer methods
(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=10.1.1.50.7868
http://www.infm.ulst.ac.uk/research/preprints/prl29apr97.ps.gz
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.50.7868
http://www.infm.ulst.ac.uk/research/preprints/prl29apr97.ps.gz
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
(66) Raftery, E B
(61) The Pennsylvania State University CiteSeerX...
(33) Lahiri, A
(25) Adrian E. Raftery
(24) A. E. Raftery
(16) Cashman, P M
(16) Gould, B A
(15) C. Fraley
(13) Raftery, E. B.
(11) Davies, A B
(10) David Madigan
(8) Lahiri, A.
(8) Mann, S
(8) Michael A. Newton
(8) Raftery, A. E.
(8) Raftery, Adrian E
(7) Bao, Le
(7) Hornung, R S
(7) Jaya M. Satagopan
(7) Jennifer A. Hoeting
(7) LAHIRI, A.
(7) Raftery, A E
(7) Raftery, Adrian E.
(6) Altman, D G
(6) Balasubramanian, V
(6) Bowles, M J
(6) Brown, Tim
(6) Chris T. Volinsky
(6) Goldberg, A D
(6) Jones, R I
(6) O'Hara, M J
(6) Pavel N. Krivitsky
(6) RAFTERY, E. B.
(6) Salomon, Joshua A
(6) Subramanian, V B
(5) Alkema, L
(5) Baker, E.
(5) Beer, A.
(5) Patrick Gerl
(5) Raftery, J
(5) Raftery, J.
(5) Raftery, P.
(4) AlKhawaja, I
(4) Basu, S.
(4) Brown, T
(4) Craig, M W
(4) Crawley, J C
(4) Denman, A M
(4) Hoeting, Jennifer A.
(4) Hogan, Daniel R
(4) J. G. Campbell
(4) Jones, R
(4) K. Y. Yeung
(4) Kieso, H A
(4) Raftery, Daniel
(4) Senior, R.
(4) Stott, F D
(4) Volinsky, Chris T.
(4) Wood, G.
(3) A E Raftery
(3) A. Murua
(3) Adams, J
(3) Adrian E Raftery
(3) Baggaley, Rebecca F
(3) Bala Subramanian, V
(3) Baldwin, A
(3) Ballard, C
(3) Banerjee, S
(3) Barber, B
(3) Bentham, P
(3) Brown, R
(3) Burns, A
(3) Caruana, M
(3) Dening, T
(3) Diderichsen, F.
(3) Dore, C
(3) Email Alerting
(3) F. Murtagh
(3) Fernandez, E.
(3) Findlay, D
(3) Gerland, Patrick
(3) Gouws, E
(3) Gray, R
(3) Griffin, M
(3) Holmes, C
(3) Howard, R
(3) Hughes, A
(3) Jacoby, R
(3) Johnson, T
(3) Joshua A Salomon
(3) Juszczak, E
(3) Kieso, H
(3) Knapp, M
(3) L Alkema
(3) Le Bao
(3) Lindesay, J
(3) Macharouthu, A
(3) Maintainer N. Dean
(3) McKeith, I
(3) Melville, D I
Author:
Subject
(63) research article
(11) articles
(10) supplement
(6) bayesian model averaging
(6) correspondence
(5) key words
(4) article
(4) contents
(4) humans
(4) model uncertainty
(4) original papers
(3) east west
(3) health studies
(3) model selection
(3) statistics
(2) adolescent
(2) adult
(2) aged
(2) alzheimer disease
(2) bayes factor
(2) bayesian cluster analysis
(2) bayesian graphical models
(2) bayesian melding
(2) change point transformation
(2) deldir 0 0 10 suggests gpclib
(2) depends r 2 10 0
(2) dopamine agents
(2) evidence based medicine
(2) graphics
(2) including
(2) indans
(2) industrial inspection
(2) learning
(2) letter
(2) machine vision
(2) mainly spatial point patterns
(2) maptools
(2) markov chain monte carlo
(2) markov chain monte carlo model composition
(2) memantine
(2) mgcv
(2) middle aged
(2) nootropic agents
(2) papers and originals
(2) pattern recognition
(2) piperidines
(2) posterior model probability
(2) primary care
(2) research design
(2) rpanel
(2) scatterplot3d description a package for...
(2) severity of illness index
(2) sm
(2) stats
(2) tkrplot
(2) utils
(1) 60g55
(1) 62f03
(1) 62f04
(1) 62f05
(1) 62f10
(1) 62f11
(1) 62f12
(1) 62f25
(1) 62m02
(1) 62m05
(1) 80 and over
(1) age
(1) aids
(1) aldol reaction
(1) aldopentoses
(1) alzheimer s disease
(1) analysis of variance
(1) anti tnf
(1) asthma
(1) astrophysics and astronomy
(1) astrophysics instrumentation and methods for...
(1) astrophysics solar and stellar astrophysics
(1) audiovisual aids
(1) autoimmune diseases
(1) basic research
(1) bayes factors
(1) bayesian hierarchical model two sex model...
(1) bayesian model averaging bayesian graphical...
(1) beta binomial
(1) black root rot
(1) breathing exercises
(1) calcium antagonists in the treatment of angina
(1) candida albicans
(1) cardiovascular health study i contents
(1) cardiovascular health studyi contents
(1) case reports
(1) cells
(1) chalara elegans
(1) clinical and diagnostic laboratory immunology
(1) clinical investigations
(1) clinical protocols
(1) coagulation
(1) coal mining disasters
(1) comment
Subject:
Dewey Decimal Classification (DDC)
(33) Statistics [31*]
(20) Medicine & health [61*]
(5) Computer science, knowledge & systems [00*]
(5) Sports, games & entertainment [79*]
(4) Social problems & social services [36*]
(3) Agriculture [63*]
(2) Science [50*]
(2) Mathematics [51*]
(1) Psychology [15*]
(1) Earth sciences & geology [55*]
(1) Life sciences; biology [57*]
Dewey Decimal Classification (DDC):
Year of Publication
(37) 2009
(22) 2013
(14) 2012
(13) 2008
(12) 1982
(10) 1986
(9) 2010
(8) 2011
(7) 1980
(6) 1981
(6) 1983
(6) 1985
(5) 1984
(5) 1989
(4) 1979
(4) 1988
(4) 2001
(4) 2006
(3) 1974
(3) 1977
(3) 1987
(3) 1995
(3) 1999
(3) 2005
(3) 2014
(2) 1975
(2) 1976
(2) 1978
(2) 1990
(2) 1991
(2) 1992
(2) 1993
(2) 1994
(2) 1996
(2) 1998
(1) 1968
(1) 1973
(1) 2000
(1) 2002
(1) 2003
(1) 2007
Year of Publication:
Content Provider
(62) PubMed Central
(61) CiteSeerX
(59) HighWire Press
(5) Adelaide Univ.: Digital Library
(4) RePEc.org
(4) London Univ. College: UCL Discovery
(4) Manchester Univ.: eScholar Services
(3) ArXiv.org
(3) Project Euclid
(2) Deakin Univ.: Deakin Research Online
(2) Harvard Univ.: DASH
(2) Royal Melbourne Inst. of Tech. (RMIT)
(2) Oxford Univ.: Research Archive (ORA)
(1) Auckland Univ. Tech.
(1) Caltech: Authors
(1) CERN (Switzerland)
(1) Collection of Biostatistics Research Archiv
(1) Columbia Univ.
(1) DataCite Metadata Store
(1) Leicester Univ.
(1) Munich LMU: Open Access
(1) Southampton Univ.: ePrints Soton
(1) Glasgow Univ.
(1) Michigan Univ.: Deep Blue
(1) Hong Kong Univ.: HKU Scholars Hub
(1) North Texas Univ.: UNT Digital Library
(1) Queensland Univ.: UQ eSpace
Content Provider:
Language
(203) English
(24) Unknown
Language:
Document Type
(191) Text
(28) Article, Journals
(6) Reports, Papers, Lectures
(2) Unknown
Document Type:
Access
(185) Open Access
(42) 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

6

7

8

9

10

11
Next »
[23]
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