Local Statistical Modeling via a Cluster-Weighted Approach with Elliptical Distributions
Abstract Cluster-weighted modeling (CWM) is a mixture approach to modeling the joint probability of data coming from a heterogeneous population. Under Gaussian assumptions, we investigate statistical properties of CWM from both theoretical and numerical point of view; in particular, we show that Gau...
Ausführliche Beschreibung
Autor*in: |
Ingrassia, Salvatore [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
Erschienen: |
2012 |
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Schlagwörter: |
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Anmerkung: |
© Springer Science+Business Media, LLC 2012 |
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Übergeordnetes Werk: |
Enthalten in: Journal of classification - Springer-Verlag, 1984, 29(2012), 3 vom: 23. Aug., Seite 363-401 |
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Übergeordnetes Werk: |
volume:29 ; year:2012 ; number:3 ; day:23 ; month:08 ; pages:363-401 |
Links: |
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DOI / URN: |
10.1007/s00357-012-9114-3 |
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Katalog-ID: |
OLC2062463847 |
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10.1007/s00357-012-9114-3 doi (DE-627)OLC2062463847 (DE-He213)s00357-012-9114-3-p DE-627 ger DE-627 rakwb eng 150 510 600 VZ 24,1 ssgn Ingrassia, Salvatore verfasserin aut Local Statistical Modeling via a Cluster-Weighted Approach with Elliptical Distributions 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2012 Abstract Cluster-weighted modeling (CWM) is a mixture approach to modeling the joint probability of data coming from a heterogeneous population. Under Gaussian assumptions, we investigate statistical properties of CWM from both theoretical and numerical point of view; in particular, we show that Gaussian CWM includes mixtures of distributions and mixtures of regressions as special cases. Further, we introduce CWM based on Student-t distributions, which provides a more robust fit for groups of observations with longer than normal tails or noise data. Theoretical results are illustrated using some empirical studies, considering both simulated and real data. Some generalizations of such models are also outlined. Cluster-weighted modeling Mixture models Model-based clustering Minotti, Simona C. aut Vittadini, Giorgio aut Enthalten in Journal of classification Springer-Verlag, 1984 29(2012), 3 vom: 23. Aug., Seite 363-401 (DE-627)129337323 (DE-600)142885-8 (DE-576)014642832 0176-4268 nnns volume:29 year:2012 number:3 day:23 month:08 pages:363-401 https://doi.org/10.1007/s00357-012-9114-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-BUB SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_11 GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4277 AR 29 2012 3 23 08 363-401 |
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Abstract Cluster-weighted modeling (CWM) is a mixture approach to modeling the joint probability of data coming from a heterogeneous population. Under Gaussian assumptions, we investigate statistical properties of CWM from both theoretical and numerical point of view; in particular, we show that Gaussian CWM includes mixtures of distributions and mixtures of regressions as special cases. Further, we introduce CWM based on Student-t distributions, which provides a more robust fit for groups of observations with longer than normal tails or noise data. Theoretical results are illustrated using some empirical studies, considering both simulated and real data. Some generalizations of such models are also outlined. © Springer Science+Business Media, LLC 2012 |
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Abstract Cluster-weighted modeling (CWM) is a mixture approach to modeling the joint probability of data coming from a heterogeneous population. Under Gaussian assumptions, we investigate statistical properties of CWM from both theoretical and numerical point of view; in particular, we show that Gaussian CWM includes mixtures of distributions and mixtures of regressions as special cases. Further, we introduce CWM based on Student-t distributions, which provides a more robust fit for groups of observations with longer than normal tails or noise data. Theoretical results are illustrated using some empirical studies, considering both simulated and real data. Some generalizations of such models are also outlined. © Springer Science+Business Media, LLC 2012 |
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Abstract Cluster-weighted modeling (CWM) is a mixture approach to modeling the joint probability of data coming from a heterogeneous population. Under Gaussian assumptions, we investigate statistical properties of CWM from both theoretical and numerical point of view; in particular, we show that Gaussian CWM includes mixtures of distributions and mixtures of regressions as special cases. Further, we introduce CWM based on Student-t distributions, which provides a more robust fit for groups of observations with longer than normal tails or noise data. Theoretical results are illustrated using some empirical studies, considering both simulated and real data. Some generalizations of such models are also outlined. © Springer Science+Business Media, LLC 2012 |
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10.1007/s00357-012-9114-3 |
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2024-07-03T15:09:44.520Z |
_version_ |
1803571046143492096 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2062463847</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230331141146.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2012 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00357-012-9114-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2062463847</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00357-012-9114-3-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">150</subfield><subfield code="a">510</subfield><subfield code="a">600</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">24,1</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ingrassia, Salvatore</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Local Statistical Modeling via a Cluster-Weighted Approach with Elliptical Distributions</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</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 Science+Business Media, LLC 2012</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Cluster-weighted modeling (CWM) is a mixture approach to modeling the joint probability of data coming from a heterogeneous population. Under Gaussian assumptions, we investigate statistical properties of CWM from both theoretical and numerical point of view; in particular, we show that Gaussian CWM includes mixtures of distributions and mixtures of regressions as special cases. Further, we introduce CWM based on Student-t distributions, which provides a more robust fit for groups of observations with longer than normal tails or noise data. Theoretical results are illustrated using some empirical studies, considering both simulated and real data. 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