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

Gespeichert in:

Autor*in:	Miljkovic, Tatjana [verfasserIn] Fernández, Daniel [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	June 2018

Übergeordnetes Werk:	Enthalten in: Risks - Basel : MDPI, 2013, 6(2018), 2 vom: Juni, Seite 1-18
Übergeordnetes Werk:	volume:6 ; year:2018 ; number:2 ; month:06 ; pages:1-18

Links:	Volltext Volltext Volltext Terms of use

DOI / URN:	10.3390/risks6020057

Katalog-ID:	1025641205

Internformat


LEADER	01000caa a2200265 4500
001	1025641205
003	DE-627
005	20210903192129.0
007	cr uuu---uuuuu
008	180704s2018 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.3390/risks6020057 \|2 doi
024	7		\|a 10419/195849 \|2 hdl
035			\|a (DE-627)1025641205
035			\|a (DE-599)GBV1025641205
040			\|a DE-627 \|b ger \|c DE-627 \|e rda
041			\|a eng
084			\|a C02 \|a C40 \|a C60 \|2 jelc
100	1		\|a Miljkovic, Tatjana \|e verfasserin \|4 aut
245	1	0	\|a On two mixture-based clustering approaches used in modeling an insurance portfolio \|c Tatjana Miljkovic and Daniel Fernández
264		1	\|c June 2018
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a 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.
700	1		\|a Fernández, Daniel \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Risks \|d Basel : MDPI, 2013 \|g 6(2018), 2 vom: Juni, Seite 1-18 \|h Online-Ressource \|w (DE-627)737288485 \|w (DE-600)2704357-5 \|w (DE-576)379467852 \|x 2227-9091 \|7 nnns
773	1	8	\|g volume:6 \|g year:2018 \|g number:2 \|g month:06 \|g pages:1-18
856	4	0	\|u http://hdl.handle.net/10419/195849 \|x Resolving-System \|z kostenfrei \|3 Volltext
856	4	0	\|u https://doi.org/10.3390/risks6020057 \|x Resolving-System \|z kostenfrei \|3 Volltext
856	4	0	\|u http://www.mdpi.com/2227-9091/6/2/57/pdf \|x Verlag \|z kostenfrei \|3 Volltext
856	4	2	\|u http://creativecommons.org/licenses/by/4.0/ \|x Verlag \|y Terms of use \|3 46
912			\|a GBV_USEFLAG_U
912			\|a GBV_ILN_26
912			\|a ISIL_DE-206
912			\|a SYSFLAG_1
912			\|a GBV_KXP
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_152
912			\|a GBV_ILN_161
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
935			\|i zbwolc20181124
951			\|a AR
952			\|d 6 \|j 2018 \|e 2 \|c 6 \|h 1-18
980			\|2 26 \|1 01 \|x 0206 \|b 178163551X \|y x1k \|z 04-07-18
982			\|2 26 \|1 00 \|x DE-206 \|8 56 \|a generalized linear model
982			\|2 26 \|1 00 \|x DE-206 \|8 56 \|a cluster-weighted model
982			\|2 26 \|1 00 \|x DE-206 \|8 56 \|a ordered stereotype model
982			\|2 26 \|1 00 \|x DE-206 \|8 56 \|a ordinal data

Indexfelder

author_variant	t m tm d f df
matchkey_str	article:22279091:2018----::nwmxueaecutrnapoceueimdln
hierarchy_sort_str	June 2018
publishDate	2018
allfields	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
spelling	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
allfields_unstemmed	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
allfieldsGer	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
allfieldsSound	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
language	English
source	Enthalten in Risks 6(2018), 2 vom: Juni, Seite 1-18 volume:6 year:2018 number:2 month:06 pages:1-18
sourceStr	Enthalten in Risks 6(2018), 2 vom: Juni, Seite 1-18 volume:6 year:2018 number:2 month:06 pages:1-18
format_phy_str_mv	Article
building	26:1
institution	findex.gbv.de
selectbib_iln_str_mv	26@1k
sw_local_iln_str_mv	26:generalized linear model DE-206:generalized linear model 26:cluster-weighted model DE-206:cluster-weighted model 26:ordered stereotype model DE-206:ordered stereotype model 26:ordinal data DE-206:ordinal data
isfreeaccess_bool	true
container_title	Risks
authorswithroles_txt_mv	Miljkovic, Tatjana @@aut@@ Fernández, Daniel @@aut@@
publishDateDaySort_date	2018-06-01T00:00:00Z
hierarchy_top_id	737288485
id	1025641205
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">1025641205</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20210903192129.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180704s2018 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/risks6020057</subfield><subfield code="2">doi</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10419/195849</subfield><subfield code="2">hdl</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1025641205</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBV1025641205</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">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">C02</subfield><subfield code="a">C40</subfield><subfield code="a">C60</subfield><subfield code="2">jelc</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Miljkovic, Tatjana</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">On two mixture-based clustering approaches used in modeling an insurance portfolio</subfield><subfield code="c">Tatjana Miljkovic and Daniel Fernández</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">June 2018</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">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fernández, Daniel</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Risks</subfield><subfield code="d">Basel : MDPI, 2013</subfield><subfield code="g">6(2018), 2 vom: Juni, Seite 1-18</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)737288485</subfield><subfield code="w">(DE-600)2704357-5</subfield><subfield code="w">(DE-576)379467852</subfield><subfield code="x">2227-9091</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:6</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:2</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:1-18</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://hdl.handle.net/10419/195849</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/risks6020057</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.mdpi.com/2227-9091/6/2/57/pdf</subfield><subfield code="x">Verlag</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://creativecommons.org/licenses/by/4.0/</subfield><subfield code="x">Verlag</subfield><subfield code="y">Terms of use</subfield><subfield code="3">46</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_KXP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="935" ind1=" " ind2=" "><subfield code="i">zbwolc20181124</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">6</subfield><subfield code="j">2018</subfield><subfield code="e">2</subfield><subfield code="c">6</subfield><subfield code="h">1-18</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">178163551X</subfield><subfield code="y">x1k</subfield><subfield code="z">04-07-18</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">generalized linear model</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">cluster-weighted model</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">ordered stereotype model</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">ordinal data</subfield></datafield></record></collection>
author	Miljkovic, Tatjana
spellingShingle	Miljkovic, Tatjana jelc C02 26 generalized linear model 26 cluster-weighted model 26 ordered stereotype model 26 ordinal data On two mixture-based clustering approaches used in modeling an insurance portfolio
authorStr	Miljkovic, Tatjana
ppnlink_with_tag_str_mv	@@773@@(DE-627)737288485
format	electronic Article
delete_txt_mv	keep
author_role	aut aut
collection	KXP GVK SWB
remote_str	true
last_changed_iln_str_mv	26@04-07-18
illustrated	Not Illustrated
issn	2227-9091
topic_title	C02 C40 C60 jelc 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 On two mixture-based clustering approaches used in modeling an insurance portfolio Tatjana Miljkovic and Daniel Fernández
topic	jelc C02 26 generalized linear model 26 cluster-weighted model 26 ordered stereotype model 26 ordinal data
topic_unstemmed	jelc C02 26 generalized linear model 26 cluster-weighted model 26 ordered stereotype model 26 ordinal data
topic_browse	jelc C02 26 generalized linear model 26 cluster-weighted model 26 ordered stereotype model 26 ordinal data
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Risks
hierarchy_parent_id	737288485
hierarchy_top_title	Risks
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)737288485 (DE-600)2704357-5 (DE-576)379467852
title	On two mixture-based clustering approaches used in modeling an insurance portfolio
ctrlnum	(DE-627)1025641205 (DE-599)GBV1025641205
title_full	On two mixture-based clustering approaches used in modeling an insurance portfolio Tatjana Miljkovic and Daniel Fernández
author_sort	Miljkovic, Tatjana
journal	Risks
journalStr	Risks
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2018
contenttype_str_mv	txt
container_start_page	1
author_browse	Miljkovic, Tatjana Fernández, Daniel
selectkey	26:x
container_volume	6
class	C02 C40 C60 jelc
format_se	Elektronische Aufsätze
author-letter	Miljkovic, Tatjana
doi_str_mv	10.3390/risks6020057
author2-role	verfasserin
title_sort	on two mixture-based clustering approaches used in modeling an insurance portfolio
title_auth	On two mixture-based clustering approaches used in modeling an insurance portfolio
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.
collection_details	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
container_issue	2
title_short	On two mixture-based clustering approaches used in modeling an insurance portfolio
url	http://hdl.handle.net/10419/195849 https://doi.org/10.3390/risks6020057 http://www.mdpi.com/2227-9091/6/2/57/pdf http://creativecommons.org/licenses/by/4.0/
ausleihindikator_str_mv	26
remote_bool	true
author2	Fernández, Daniel
author2Str	Fernández, Daniel
ppnlink	737288485
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.3390/risks6020057
up_date	2024-07-04T14:47:39.315Z
_version_	1803660253533831168
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">1025641205</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20210903192129.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180704s2018 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/risks6020057</subfield><subfield code="2">doi</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10419/195849</subfield><subfield code="2">hdl</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1025641205</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBV1025641205</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">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">C02</subfield><subfield code="a">C40</subfield><subfield code="a">C60</subfield><subfield code="2">jelc</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Miljkovic, Tatjana</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">On two mixture-based clustering approaches used in modeling an insurance portfolio</subfield><subfield code="c">Tatjana Miljkovic and Daniel Fernández</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">June 2018</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">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fernández, Daniel</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Risks</subfield><subfield code="d">Basel : MDPI, 2013</subfield><subfield code="g">6(2018), 2 vom: Juni, Seite 1-18</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)737288485</subfield><subfield code="w">(DE-600)2704357-5</subfield><subfield code="w">(DE-576)379467852</subfield><subfield code="x">2227-9091</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:6</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:2</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:1-18</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://hdl.handle.net/10419/195849</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/risks6020057</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.mdpi.com/2227-9091/6/2/57/pdf</subfield><subfield code="x">Verlag</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://creativecommons.org/licenses/by/4.0/</subfield><subfield code="x">Verlag</subfield><subfield code="y">Terms of use</subfield><subfield code="3">46</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_KXP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="935" ind1=" " ind2=" "><subfield code="i">zbwolc20181124</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">6</subfield><subfield code="j">2018</subfield><subfield code="e">2</subfield><subfield code="c">6</subfield><subfield code="h">1-18</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">178163551X</subfield><subfield code="y">x1k</subfield><subfield code="z">04-07-18</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">generalized linear model</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">cluster-weighted model</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">ordered stereotype model</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">ordinal data</subfield></datafield></record></collection>
score	7.3994474

Nicht das Richtige dabei?

Schreiben Sie uns!

On two mixture-based clustering approaches used in modeling an insurance portfolio

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?