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Title:

Modeling outliers, bursts and flat stretches in time series using Mixture Transition Distribution (MTD) models

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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 non-Gaussian and non-linear 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 non-Gaussian and non-linear 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 non-standard 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

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-08-06

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)*

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References Open Access Rapid responses

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Bayesian melding for estimating uncertainty in

Bayesian melding for estimating uncertainty in Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-07-17

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

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Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

MCLUST: Software for Model-Based Cluster Analysis

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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 N00014-96-1-0192 and N00014-96-1-0330. 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

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-11

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

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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

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Title:

How many clusters? Which clustering method? Answers via model-based cluster analysis

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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 cross-cluster constraints. Noise and outliers can be modeled by adding a Poisson process component. Partitions are determined by the EM (expectation-maximization) 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

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-01-06

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

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Contents

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DDC:

310 Collections of general statistics *(computed)*

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Title:

Linear Flaw Detection in Woven Textiles using Model-Based Clustering

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We combine image-processing techniques with a powerful new statistical methodology to test for and find the location of linear production faults in woven textiles. Our approach detects an alignment pattern in preprocessed images via model-based clustering and uses an approximate Bayes factor to assess the evidence for the presence of a defect. R...

We combine image-processing techniques with a powerful new statistical methodology to test for and find the location of linear production faults in woven textiles. Our approach detects an alignment pattern in preprocessed images via model-based clustering and uses an approximate Bayes factor to assess the evidence for the presence of a defect. Results are shown for some representative examples, and the associated software has been made available on the Internet. Keywords. Model-based clustering, pattern recognition, Bayesian cluster analysis, machine vision, industrial inspection. Supported by Office of Naval Research under contracts N-00014-96-1-0192 and N-00014-96-1-0330. y Corresponding author. 1 Signal and Image Processing Group, Interactive Systems Centre, University of Ulster, Magee College, Londonderry BT48 7JL, Northern Ireland. Email: jg.campbell@ulst.ac.uk 2 Department of Statistics, Box 354322, University of Washington, Seattle, WA 98195-4322, USA. Email: fraley@stat. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-12

Source:

http://www.ece.nwu.edu/~harsha/Clustering/tr314.ps

http://www.ece.nwu.edu/~harsha/Clustering/tr314.ps Minimize

Document Type:

text

Language:

en

DDC:

006 Special computer methods *(computed)*

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Title:

How many clusters? Which clustering method? - Answers via model-based cluster analysis

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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 cross-cluster constraints. Noise and outliers can be modeled by adding a Poisson process component. Partitions are determined by the EM (expectation-maximization) 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. Examples are given, showing that this approach can give performance that is much better Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-08-06

Source:

http://www.stat.washington.edu/research/reports/1998/tr329.pdf

http://www.stat.washington.edu/research/reports/1998/tr329.pdf Minimize

Document Type:

text

Language:

en

DDC:

006 Special computer methods *(computed)*

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Title:

Choosing the Link Function and Accounting for Link Uncertainty in Generalized Linear Models using Bayes Factors

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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), Aranda-Ordaz (1981) , Guerrero and Johnson (1982), Morgan (1983) and Whittmore (1983) proposed one-parameter families, while Prentice (1976), Pregibon (1980), Stukel (1988) and Czado (1992) considered two-parameter families. Link functions for the non-binary case were studied by Pregibon (1980) and Czado (1992, 1997) Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-17

Source:

http://www-m4.mathematik.tu-muenchen.de/m4/Papers/Czado/bayes.ps

http://www-m4.mathematik.tu-muenchen.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)*

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Title:

How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis

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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 model-based 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 model-based 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 cross-cluster constraints. Noise can be modeled by adding a Poisson component. Partitions are determined by the EM (expectation-maximization) 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:

2009-04-11

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)*

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Title:

biocViews Microarray, TwoChannel, DifferentialExpression Imports stats

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Description Package for normalizing microarray data in single and multiple replicate experiments and fitting a normal-uniform 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 normal-uniform 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

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-10-15

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

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

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Title:

Linear Flaw Detection in Woven Textiles using Model-Based Clustering

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Description:

We combine image-processing 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 model-based clustering. It employs an approximate Bayes factor which provides a criterion for assessing the evidence for the presence of a ...

We combine image-processing 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 model-based 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 Model-based 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

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-12

Source:

http://www.infm.ulst.ac.uk/research/preprints/prl-29apr97.ps.gz

http://www.infm.ulst.ac.uk/research/preprints/prl-29apr97.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)*

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