Clustering bivariate mixed-type data via the cluster-weighted model
Abstract The cluster-weighted model (CWM) is a mixture model with random covariates that allows for flexible clustering/classification and distribution estimation of a random vector composed of a response variable and a set of covariates. Within this class of models, the generalized linear exponenti...
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Gespeichert in:
| Autor*in: |
Punzo, Antonio [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag Berlin Heidelberg 2015 |
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| Übergeordnetes Werk: |
Enthalten in: Computational statistics - Springer Berlin Heidelberg, 1992, 31(2015), 3 vom: 04. Juli, Seite 989-1013 |
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| Übergeordnetes Werk: |
volume:31 ; year:2015 ; number:3 ; day:04 ; month:07 ; pages:989-1013 |
| Links: |
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| DOI / URN: |
10.1007/s00180-015-0600-z |
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OLC2070884740 |
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| 520 | |a Abstract The cluster-weighted model (CWM) is a mixture model with random covariates that allows for flexible clustering/classification and distribution estimation of a random vector composed of a response variable and a set of covariates. Within this class of models, the generalized linear exponential CWM is here introduced especially for modeling bivariate data of mixed-type. Its natural counterpart in the family of latent class models is also defined. Maximum likelihood parameter estimates are derived using the expectation-maximization algorithm and some computational issues are detailed. Through Monte Carlo experiments, the classification performance of the proposed model is compared with other mixture-based approaches, consistency of the estimators of the regression coefficients is evaluated, and several likelihood-based information criteria are compared for selecting the number of mixture components. An application to real data is also finally considered. | ||
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10.1007/s00180-015-0600-z doi (DE-627)OLC2070884740 (DE-He213)s00180-015-0600-z-p DE-627 ger DE-627 rakwb eng 510 004 VZ Punzo, Antonio verfasserin aut Clustering bivariate mixed-type data via the cluster-weighted model 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2015 Abstract The cluster-weighted model (CWM) is a mixture model with random covariates that allows for flexible clustering/classification and distribution estimation of a random vector composed of a response variable and a set of covariates. Within this class of models, the generalized linear exponential CWM is here introduced especially for modeling bivariate data of mixed-type. Its natural counterpart in the family of latent class models is also defined. Maximum likelihood parameter estimates are derived using the expectation-maximization algorithm and some computational issues are detailed. Through Monte Carlo experiments, the classification performance of the proposed model is compared with other mixture-based approaches, consistency of the estimators of the regression coefficients is evaluated, and several likelihood-based information criteria are compared for selecting the number of mixture components. An application to real data is also finally considered. Mixture models with random covariates Model-based clustering Cluster-weighted models Generalized linear models Mixed-type data Ingrassia, Salvatore aut Enthalten in Computational statistics Springer Berlin Heidelberg, 1992 31(2015), 3 vom: 04. Juli, Seite 989-1013 (DE-627)131054694 (DE-600)1104678-8 (DE-576)028053559 0943-4062 nnns volume:31 year:2015 number:3 day:04 month:07 pages:989-1013 https://doi.org/10.1007/s00180-015-0600-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_2088 GBV_ILN_4305 AR 31 2015 3 04 07 989-1013 |
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10.1007/s00180-015-0600-z doi (DE-627)OLC2070884740 (DE-He213)s00180-015-0600-z-p DE-627 ger DE-627 rakwb eng 510 004 VZ Punzo, Antonio verfasserin aut Clustering bivariate mixed-type data via the cluster-weighted model 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2015 Abstract The cluster-weighted model (CWM) is a mixture model with random covariates that allows for flexible clustering/classification and distribution estimation of a random vector composed of a response variable and a set of covariates. Within this class of models, the generalized linear exponential CWM is here introduced especially for modeling bivariate data of mixed-type. Its natural counterpart in the family of latent class models is also defined. Maximum likelihood parameter estimates are derived using the expectation-maximization algorithm and some computational issues are detailed. Through Monte Carlo experiments, the classification performance of the proposed model is compared with other mixture-based approaches, consistency of the estimators of the regression coefficients is evaluated, and several likelihood-based information criteria are compared for selecting the number of mixture components. An application to real data is also finally considered. Mixture models with random covariates Model-based clustering Cluster-weighted models Generalized linear models Mixed-type data Ingrassia, Salvatore aut Enthalten in Computational statistics Springer Berlin Heidelberg, 1992 31(2015), 3 vom: 04. Juli, Seite 989-1013 (DE-627)131054694 (DE-600)1104678-8 (DE-576)028053559 0943-4062 nnns volume:31 year:2015 number:3 day:04 month:07 pages:989-1013 https://doi.org/10.1007/s00180-015-0600-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_2088 GBV_ILN_4305 AR 31 2015 3 04 07 989-1013 |
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10.1007/s00180-015-0600-z doi (DE-627)OLC2070884740 (DE-He213)s00180-015-0600-z-p DE-627 ger DE-627 rakwb eng 510 004 VZ Punzo, Antonio verfasserin aut Clustering bivariate mixed-type data via the cluster-weighted model 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2015 Abstract The cluster-weighted model (CWM) is a mixture model with random covariates that allows for flexible clustering/classification and distribution estimation of a random vector composed of a response variable and a set of covariates. Within this class of models, the generalized linear exponential CWM is here introduced especially for modeling bivariate data of mixed-type. Its natural counterpart in the family of latent class models is also defined. Maximum likelihood parameter estimates are derived using the expectation-maximization algorithm and some computational issues are detailed. Through Monte Carlo experiments, the classification performance of the proposed model is compared with other mixture-based approaches, consistency of the estimators of the regression coefficients is evaluated, and several likelihood-based information criteria are compared for selecting the number of mixture components. An application to real data is also finally considered. Mixture models with random covariates Model-based clustering Cluster-weighted models Generalized linear models Mixed-type data Ingrassia, Salvatore aut Enthalten in Computational statistics Springer Berlin Heidelberg, 1992 31(2015), 3 vom: 04. Juli, Seite 989-1013 (DE-627)131054694 (DE-600)1104678-8 (DE-576)028053559 0943-4062 nnns volume:31 year:2015 number:3 day:04 month:07 pages:989-1013 https://doi.org/10.1007/s00180-015-0600-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_2088 GBV_ILN_4305 AR 31 2015 3 04 07 989-1013 |
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10.1007/s00180-015-0600-z doi (DE-627)OLC2070884740 (DE-He213)s00180-015-0600-z-p DE-627 ger DE-627 rakwb eng 510 004 VZ Punzo, Antonio verfasserin aut Clustering bivariate mixed-type data via the cluster-weighted model 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2015 Abstract The cluster-weighted model (CWM) is a mixture model with random covariates that allows for flexible clustering/classification and distribution estimation of a random vector composed of a response variable and a set of covariates. Within this class of models, the generalized linear exponential CWM is here introduced especially for modeling bivariate data of mixed-type. Its natural counterpart in the family of latent class models is also defined. Maximum likelihood parameter estimates are derived using the expectation-maximization algorithm and some computational issues are detailed. Through Monte Carlo experiments, the classification performance of the proposed model is compared with other mixture-based approaches, consistency of the estimators of the regression coefficients is evaluated, and several likelihood-based information criteria are compared for selecting the number of mixture components. An application to real data is also finally considered. Mixture models with random covariates Model-based clustering Cluster-weighted models Generalized linear models Mixed-type data Ingrassia, Salvatore aut Enthalten in Computational statistics Springer Berlin Heidelberg, 1992 31(2015), 3 vom: 04. Juli, Seite 989-1013 (DE-627)131054694 (DE-600)1104678-8 (DE-576)028053559 0943-4062 nnns volume:31 year:2015 number:3 day:04 month:07 pages:989-1013 https://doi.org/10.1007/s00180-015-0600-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_2088 GBV_ILN_4305 AR 31 2015 3 04 07 989-1013 |
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Abstract The cluster-weighted model (CWM) is a mixture model with random covariates that allows for flexible clustering/classification and distribution estimation of a random vector composed of a response variable and a set of covariates. Within this class of models, the generalized linear exponential CWM is here introduced especially for modeling bivariate data of mixed-type. Its natural counterpart in the family of latent class models is also defined. Maximum likelihood parameter estimates are derived using the expectation-maximization algorithm and some computational issues are detailed. Through Monte Carlo experiments, the classification performance of the proposed model is compared with other mixture-based approaches, consistency of the estimators of the regression coefficients is evaluated, and several likelihood-based information criteria are compared for selecting the number of mixture components. An application to real data is also finally considered. © Springer-Verlag Berlin Heidelberg 2015 |
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Abstract The cluster-weighted model (CWM) is a mixture model with random covariates that allows for flexible clustering/classification and distribution estimation of a random vector composed of a response variable and a set of covariates. Within this class of models, the generalized linear exponential CWM is here introduced especially for modeling bivariate data of mixed-type. Its natural counterpart in the family of latent class models is also defined. Maximum likelihood parameter estimates are derived using the expectation-maximization algorithm and some computational issues are detailed. Through Monte Carlo experiments, the classification performance of the proposed model is compared with other mixture-based approaches, consistency of the estimators of the regression coefficients is evaluated, and several likelihood-based information criteria are compared for selecting the number of mixture components. An application to real data is also finally considered. © Springer-Verlag Berlin Heidelberg 2015 |
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Abstract The cluster-weighted model (CWM) is a mixture model with random covariates that allows for flexible clustering/classification and distribution estimation of a random vector composed of a response variable and a set of covariates. Within this class of models, the generalized linear exponential CWM is here introduced especially for modeling bivariate data of mixed-type. Its natural counterpart in the family of latent class models is also defined. Maximum likelihood parameter estimates are derived using the expectation-maximization algorithm and some computational issues are detailed. Through Monte Carlo experiments, the classification performance of the proposed model is compared with other mixture-based approaches, consistency of the estimators of the regression coefficients is evaluated, and several likelihood-based information criteria are compared for selecting the number of mixture components. An application to real data is also finally considered. © Springer-Verlag Berlin Heidelberg 2015 |
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2024-07-04T02:30:32.135Z |
| _version_ |
1803613877973286912 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2070884740</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230323142614.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2015 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00180-015-0600-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2070884740</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00180-015-0600-z-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Punzo, Antonio</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Clustering bivariate mixed-type data via the cluster-weighted model</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag Berlin Heidelberg 2015</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The cluster-weighted model (CWM) is a mixture model with random covariates that allows for flexible clustering/classification and distribution estimation of a random vector composed of a response variable and a set of covariates. Within this class of models, the generalized linear exponential CWM is here introduced especially for modeling bivariate data of mixed-type. Its natural counterpart in the family of latent class models is also defined. Maximum likelihood parameter estimates are derived using the expectation-maximization algorithm and some computational issues are detailed. Through Monte Carlo experiments, the classification performance of the proposed model is compared with other mixture-based approaches, consistency of the estimators of the regression coefficients is evaluated, and several likelihood-based information criteria are compared for selecting the number of mixture components. An application to real data is also finally considered.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mixture models with random covariates</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Model-based clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cluster-weighted models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Generalized linear models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mixed-type data</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ingrassia, Salvatore</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computational statistics</subfield><subfield code="d">Springer Berlin Heidelberg, 1992</subfield><subfield code="g">31(2015), 3 vom: 04. 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7.400819 |