On two mixture-based clustering approaches used in modeling an insurance portfolio
We review two complementary mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio: the generalized linear mixed cluster-weighted model (CWM) and mixture-based clustering for an ordered stereotype model (OSM). The latter is for modeling of ordinal variabl...
Ausführliche Beschreibung
Autor*in: |
Miljkovic, Tatjana [verfasserIn] Fernández, Daniel [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
June 2018 |
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Übergeordnetes Werk: |
Enthalten in: Risks - Basel : MDPI, 2013, 6(2018), 2 vom: Juni, Seite 1-18 |
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Übergeordnetes Werk: |
volume:6 ; year:2018 ; number:2 ; month:06 ; pages:1-18 |
Links: |
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DOI / URN: |
10.3390/risks6020057 |
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Katalog-ID: |
1025641205 |
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10.3390/risks6020057 doi 10419/195849 hdl (DE-627)1025641205 (DE-599)GBV1025641205 DE-627 ger DE-627 rda eng C02 C40 C60 jelc Miljkovic, Tatjana verfasserin aut On two mixture-based clustering approaches used in modeling an insurance portfolio Tatjana Miljkovic and Daniel Fernández June 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We review two complementary mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio: the generalized linear mixed cluster-weighted model (CWM) and mixture-based clustering for an ordered stereotype model (OSM). The latter is for modeling of ordinal variables, and the former is for modeling losses as a function of mixed-type of covariates. The article extends the idea of mixture modeling to a multivariate classification for the purpose of testing unobserved heterogeneity in an insurance portfolio. The application of both methods is illustrated on a well-known French automobile portfolio, in which the model fitting is performed using the expectation-maximization (EM) algorithm. Our findings show that these mixture-based clustering methods can be used to further test unobserved heterogeneity in an insurance portfolio and as such may be considered in insurance pricing, underwriting, and risk management. Fernández, Daniel verfasserin aut Enthalten in Risks Basel : MDPI, 2013 6(2018), 2 vom: Juni, Seite 1-18 Online-Ressource (DE-627)737288485 (DE-600)2704357-5 (DE-576)379467852 2227-9091 nnns volume:6 year:2018 number:2 month:06 pages:1-18 http://hdl.handle.net/10419/195849 Resolving-System kostenfrei Volltext https://doi.org/10.3390/risks6020057 Resolving-System kostenfrei Volltext http://www.mdpi.com/2227-9091/6/2/57/pdf Verlag kostenfrei Volltext http://creativecommons.org/licenses/by/4.0/ Verlag Terms of use 46 GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 zbwolc20181124 AR 6 2018 2 6 1-18 26 01 0206 178163551X x1k 04-07-18 26 00 DE-206 56 generalized linear model 26 00 DE-206 56 cluster-weighted model 26 00 DE-206 56 ordered stereotype model 26 00 DE-206 56 ordinal data |
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10.3390/risks6020057 doi 10419/195849 hdl (DE-627)1025641205 (DE-599)GBV1025641205 DE-627 ger DE-627 rda eng C02 C40 C60 jelc Miljkovic, Tatjana verfasserin aut On two mixture-based clustering approaches used in modeling an insurance portfolio Tatjana Miljkovic and Daniel Fernández June 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We review two complementary mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio: the generalized linear mixed cluster-weighted model (CWM) and mixture-based clustering for an ordered stereotype model (OSM). The latter is for modeling of ordinal variables, and the former is for modeling losses as a function of mixed-type of covariates. The article extends the idea of mixture modeling to a multivariate classification for the purpose of testing unobserved heterogeneity in an insurance portfolio. The application of both methods is illustrated on a well-known French automobile portfolio, in which the model fitting is performed using the expectation-maximization (EM) algorithm. Our findings show that these mixture-based clustering methods can be used to further test unobserved heterogeneity in an insurance portfolio and as such may be considered in insurance pricing, underwriting, and risk management. Fernández, Daniel verfasserin aut Enthalten in Risks Basel : MDPI, 2013 6(2018), 2 vom: Juni, Seite 1-18 Online-Ressource (DE-627)737288485 (DE-600)2704357-5 (DE-576)379467852 2227-9091 nnns volume:6 year:2018 number:2 month:06 pages:1-18 http://hdl.handle.net/10419/195849 Resolving-System kostenfrei Volltext https://doi.org/10.3390/risks6020057 Resolving-System kostenfrei Volltext http://www.mdpi.com/2227-9091/6/2/57/pdf Verlag kostenfrei Volltext http://creativecommons.org/licenses/by/4.0/ Verlag Terms of use 46 GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 zbwolc20181124 AR 6 2018 2 6 1-18 26 01 0206 178163551X x1k 04-07-18 26 00 DE-206 56 generalized linear model 26 00 DE-206 56 cluster-weighted model 26 00 DE-206 56 ordered stereotype model 26 00 DE-206 56 ordinal data |
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10.3390/risks6020057 doi 10419/195849 hdl (DE-627)1025641205 (DE-599)GBV1025641205 DE-627 ger DE-627 rda eng C02 C40 C60 jelc Miljkovic, Tatjana verfasserin aut On two mixture-based clustering approaches used in modeling an insurance portfolio Tatjana Miljkovic and Daniel Fernández June 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We review two complementary mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio: the generalized linear mixed cluster-weighted model (CWM) and mixture-based clustering for an ordered stereotype model (OSM). The latter is for modeling of ordinal variables, and the former is for modeling losses as a function of mixed-type of covariates. The article extends the idea of mixture modeling to a multivariate classification for the purpose of testing unobserved heterogeneity in an insurance portfolio. The application of both methods is illustrated on a well-known French automobile portfolio, in which the model fitting is performed using the expectation-maximization (EM) algorithm. Our findings show that these mixture-based clustering methods can be used to further test unobserved heterogeneity in an insurance portfolio and as such may be considered in insurance pricing, underwriting, and risk management. Fernández, Daniel verfasserin aut Enthalten in Risks Basel : MDPI, 2013 6(2018), 2 vom: Juni, Seite 1-18 Online-Ressource (DE-627)737288485 (DE-600)2704357-5 (DE-576)379467852 2227-9091 nnns volume:6 year:2018 number:2 month:06 pages:1-18 http://hdl.handle.net/10419/195849 Resolving-System kostenfrei Volltext https://doi.org/10.3390/risks6020057 Resolving-System kostenfrei Volltext http://www.mdpi.com/2227-9091/6/2/57/pdf Verlag kostenfrei Volltext http://creativecommons.org/licenses/by/4.0/ Verlag Terms of use 46 GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 zbwolc20181124 AR 6 2018 2 6 1-18 26 01 0206 178163551X x1k 04-07-18 26 00 DE-206 56 generalized linear model 26 00 DE-206 56 cluster-weighted model 26 00 DE-206 56 ordered stereotype model 26 00 DE-206 56 ordinal data |
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10.3390/risks6020057 doi 10419/195849 hdl (DE-627)1025641205 (DE-599)GBV1025641205 DE-627 ger DE-627 rda eng C02 C40 C60 jelc Miljkovic, Tatjana verfasserin aut On two mixture-based clustering approaches used in modeling an insurance portfolio Tatjana Miljkovic and Daniel Fernández June 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We review two complementary mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio: the generalized linear mixed cluster-weighted model (CWM) and mixture-based clustering for an ordered stereotype model (OSM). The latter is for modeling of ordinal variables, and the former is for modeling losses as a function of mixed-type of covariates. The article extends the idea of mixture modeling to a multivariate classification for the purpose of testing unobserved heterogeneity in an insurance portfolio. The application of both methods is illustrated on a well-known French automobile portfolio, in which the model fitting is performed using the expectation-maximization (EM) algorithm. Our findings show that these mixture-based clustering methods can be used to further test unobserved heterogeneity in an insurance portfolio and as such may be considered in insurance pricing, underwriting, and risk management. Fernández, Daniel verfasserin aut Enthalten in Risks Basel : MDPI, 2013 6(2018), 2 vom: Juni, Seite 1-18 Online-Ressource (DE-627)737288485 (DE-600)2704357-5 (DE-576)379467852 2227-9091 nnns volume:6 year:2018 number:2 month:06 pages:1-18 http://hdl.handle.net/10419/195849 Resolving-System kostenfrei Volltext https://doi.org/10.3390/risks6020057 Resolving-System kostenfrei Volltext http://www.mdpi.com/2227-9091/6/2/57/pdf Verlag kostenfrei Volltext http://creativecommons.org/licenses/by/4.0/ Verlag Terms of use 46 GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 zbwolc20181124 AR 6 2018 2 6 1-18 26 01 0206 178163551X x1k 04-07-18 26 00 DE-206 56 generalized linear model 26 00 DE-206 56 cluster-weighted model 26 00 DE-206 56 ordered stereotype model 26 00 DE-206 56 ordinal data |
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10.3390/risks6020057 doi 10419/195849 hdl (DE-627)1025641205 (DE-599)GBV1025641205 DE-627 ger DE-627 rda eng C02 C40 C60 jelc Miljkovic, Tatjana verfasserin aut On two mixture-based clustering approaches used in modeling an insurance portfolio Tatjana Miljkovic and Daniel Fernández June 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We review two complementary mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio: the generalized linear mixed cluster-weighted model (CWM) and mixture-based clustering for an ordered stereotype model (OSM). The latter is for modeling of ordinal variables, and the former is for modeling losses as a function of mixed-type of covariates. The article extends the idea of mixture modeling to a multivariate classification for the purpose of testing unobserved heterogeneity in an insurance portfolio. The application of both methods is illustrated on a well-known French automobile portfolio, in which the model fitting is performed using the expectation-maximization (EM) algorithm. Our findings show that these mixture-based clustering methods can be used to further test unobserved heterogeneity in an insurance portfolio and as such may be considered in insurance pricing, underwriting, and risk management. Fernández, Daniel verfasserin aut Enthalten in Risks Basel : MDPI, 2013 6(2018), 2 vom: Juni, Seite 1-18 Online-Ressource (DE-627)737288485 (DE-600)2704357-5 (DE-576)379467852 2227-9091 nnns volume:6 year:2018 number:2 month:06 pages:1-18 http://hdl.handle.net/10419/195849 Resolving-System kostenfrei Volltext https://doi.org/10.3390/risks6020057 Resolving-System kostenfrei Volltext http://www.mdpi.com/2227-9091/6/2/57/pdf Verlag kostenfrei Volltext http://creativecommons.org/licenses/by/4.0/ Verlag Terms of use 46 GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 zbwolc20181124 AR 6 2018 2 6 1-18 26 01 0206 178163551X x1k 04-07-18 26 00 DE-206 56 generalized linear model 26 00 DE-206 56 cluster-weighted model 26 00 DE-206 56 ordered stereotype model 26 00 DE-206 56 ordinal data |
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abstract |
We review two complementary mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio: the generalized linear mixed cluster-weighted model (CWM) and mixture-based clustering for an ordered stereotype model (OSM). The latter is for modeling of ordinal variables, and the former is for modeling losses as a function of mixed-type of covariates. The article extends the idea of mixture modeling to a multivariate classification for the purpose of testing unobserved heterogeneity in an insurance portfolio. The application of both methods is illustrated on a well-known French automobile portfolio, in which the model fitting is performed using the expectation-maximization (EM) algorithm. Our findings show that these mixture-based clustering methods can be used to further test unobserved heterogeneity in an insurance portfolio and as such may be considered in insurance pricing, underwriting, and risk management. |
abstractGer |
We review two complementary mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio: the generalized linear mixed cluster-weighted model (CWM) and mixture-based clustering for an ordered stereotype model (OSM). The latter is for modeling of ordinal variables, and the former is for modeling losses as a function of mixed-type of covariates. The article extends the idea of mixture modeling to a multivariate classification for the purpose of testing unobserved heterogeneity in an insurance portfolio. The application of both methods is illustrated on a well-known French automobile portfolio, in which the model fitting is performed using the expectation-maximization (EM) algorithm. Our findings show that these mixture-based clustering methods can be used to further test unobserved heterogeneity in an insurance portfolio and as such may be considered in insurance pricing, underwriting, and risk management. |
abstract_unstemmed |
We review two complementary mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio: the generalized linear mixed cluster-weighted model (CWM) and mixture-based clustering for an ordered stereotype model (OSM). The latter is for modeling of ordinal variables, and the former is for modeling losses as a function of mixed-type of covariates. The article extends the idea of mixture modeling to a multivariate classification for the purpose of testing unobserved heterogeneity in an insurance portfolio. The application of both methods is illustrated on a well-known French automobile portfolio, in which the model fitting is performed using the expectation-maximization (EM) algorithm. Our findings show that these mixture-based clustering methods can be used to further test unobserved heterogeneity in an insurance portfolio and as such may be considered in insurance pricing, underwriting, and risk management. |
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On two mixture-based clustering approaches used in modeling an insurance portfolio |
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