Project management under uncertainty beyond beta : the generalized bicubic distribution

The beta distribution has traditionally been employed in the PERT methodology and generally used for modeling bounded continuous random variables based on expert’s judgment. The impossibility of estimating four parameters from the three values provided by the expert when the beta distribution is ass...
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Autor*in:	García Peréz, José Ignacio [verfasserIn] López Martín, María del Mar [verfasserIn] García García, Catalina [verfasserIn] Sánchez Granero, Miguel Ángel [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Übergeordnetes Werk:	Enthalten in: Operations research perspectives - Amsterdam [u.a.] : Elsevier, 2014, 3(2016), Seite 67-76
Übergeordnetes Werk:	volume:3 ; year:2016 ; pages:67-76

Links:	Volltext Volltext Volltext Terms of use

DOI / URN:	10.1016/j.orp.2016.09.001

Katalog-ID:	1019501820

Internformat


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520			\|a The beta distribution has traditionally been employed in the PERT methodology and generally used for modeling bounded continuous random variables based on expert’s judgment. The impossibility of estimating four parameters from the three values provided by the expert when the beta distribution is assumed to be the underlying distribution has been widely debated. This paper presents the generalized bicubic distribution as a good alternative to the beta distribution since, when the variance depends on the mode, the generalized bicubic distribution approximates the kurtosis of the Gaussian distribution better than the beta distribution. In addition, this distribution presents good properties in the PERT methodology in relation to moderation and conservatism criteria. Two empirical applications are presented to demonstrate the adequateness of this new distribution.
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912			\|a GBV_ILN_31
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_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
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_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
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_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4393
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Indexfelder

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publishDate	2016
allfields	10.1016/j.orp.2016.09.001 doi 10419/178270 hdl (DE-627)1019501820 (DE-599)GBV1019501820 DE-627 ger DE-627 rda eng García Peréz, José Ignacio verfasserin (DE-588)171340957 (DE-627)06153174X (DE-576)132142252 aut Project management under uncertainty beyond beta the generalized bicubic distribution José García Pérez, María del Mar López Martín, Catalina García García, Miguel Ángel Sánchez Granero 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The beta distribution has traditionally been employed in the PERT methodology and generally used for modeling bounded continuous random variables based on expert’s judgment. The impossibility of estimating four parameters from the three values provided by the expert when the beta distribution is assumed to be the underlying distribution has been widely debated. This paper presents the generalized bicubic distribution as a good alternative to the beta distribution since, when the variance depends on the mode, the generalized bicubic distribution approximates the kurtosis of the Gaussian distribution better than the beta distribution. In addition, this distribution presents good properties in the PERT methodology in relation to moderation and conservatism criteria. Two empirical applications are presented to demonstrate the adequateness of this new distribution. López Martín, María del Mar verfasserin (DE-588)1158691211 (DE-627)1020751452 (DE-576)504303198 aut García García, Catalina verfasserin aut Sánchez Granero, Miguel Ángel verfasserin aut Enthalten in Operations research perspectives Amsterdam [u.a.] : Elsevier, 2014 3(2016), Seite 67-76 Online-Ressource (DE-627)826105165 (DE-600)2821932-6 (DE-576)433076496 2214-7160 nnns volume:3 year:2016 pages:67-76 http://hdl.handle.net/10419/178270 Resolving-System kostenfrei Volltext https://doi.org/10.1016/j.orp.2016.09.001 Resolving-System kostenfrei Volltext https://www.sciencedirect.com/science/article/pii/S2214716016300252/pdfft?md5=baa3bd0ff53f1d679cd845cf40fb3ea5&pid=1-s2.0-S2214716016300252-main.pdf Verlag kostenfrei Volltext http://creativecommons.org/licenses/by-nc-nd/4.0/ Verlag Terms of use 46 GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 3 2016 67-76 26 01 0206 1767049218 x1k 25-04-18 26 00 DE-206 56 Uncertainty 26 00 DE-206 56 PERT 26 00 DE-206 56 Bicubic distribution 26 00 DE-206 56 Beta distribution
spelling	10.1016/j.orp.2016.09.001 doi 10419/178270 hdl (DE-627)1019501820 (DE-599)GBV1019501820 DE-627 ger DE-627 rda eng García Peréz, José Ignacio verfasserin (DE-588)171340957 (DE-627)06153174X (DE-576)132142252 aut Project management under uncertainty beyond beta the generalized bicubic distribution José García Pérez, María del Mar López Martín, Catalina García García, Miguel Ángel Sánchez Granero 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The beta distribution has traditionally been employed in the PERT methodology and generally used for modeling bounded continuous random variables based on expert’s judgment. The impossibility of estimating four parameters from the three values provided by the expert when the beta distribution is assumed to be the underlying distribution has been widely debated. This paper presents the generalized bicubic distribution as a good alternative to the beta distribution since, when the variance depends on the mode, the generalized bicubic distribution approximates the kurtosis of the Gaussian distribution better than the beta distribution. In addition, this distribution presents good properties in the PERT methodology in relation to moderation and conservatism criteria. Two empirical applications are presented to demonstrate the adequateness of this new distribution. López Martín, María del Mar verfasserin (DE-588)1158691211 (DE-627)1020751452 (DE-576)504303198 aut García García, Catalina verfasserin aut Sánchez Granero, Miguel Ángel verfasserin aut Enthalten in Operations research perspectives Amsterdam [u.a.] : Elsevier, 2014 3(2016), Seite 67-76 Online-Ressource (DE-627)826105165 (DE-600)2821932-6 (DE-576)433076496 2214-7160 nnns volume:3 year:2016 pages:67-76 http://hdl.handle.net/10419/178270 Resolving-System kostenfrei Volltext https://doi.org/10.1016/j.orp.2016.09.001 Resolving-System kostenfrei Volltext https://www.sciencedirect.com/science/article/pii/S2214716016300252/pdfft?md5=baa3bd0ff53f1d679cd845cf40fb3ea5&pid=1-s2.0-S2214716016300252-main.pdf Verlag kostenfrei Volltext http://creativecommons.org/licenses/by-nc-nd/4.0/ Verlag Terms of use 46 GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 3 2016 67-76 26 01 0206 1767049218 x1k 25-04-18 26 00 DE-206 56 Uncertainty 26 00 DE-206 56 PERT 26 00 DE-206 56 Bicubic distribution 26 00 DE-206 56 Beta distribution
allfields_unstemmed	10.1016/j.orp.2016.09.001 doi 10419/178270 hdl (DE-627)1019501820 (DE-599)GBV1019501820 DE-627 ger DE-627 rda eng García Peréz, José Ignacio verfasserin (DE-588)171340957 (DE-627)06153174X (DE-576)132142252 aut Project management under uncertainty beyond beta the generalized bicubic distribution José García Pérez, María del Mar López Martín, Catalina García García, Miguel Ángel Sánchez Granero 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The beta distribution has traditionally been employed in the PERT methodology and generally used for modeling bounded continuous random variables based on expert’s judgment. The impossibility of estimating four parameters from the three values provided by the expert when the beta distribution is assumed to be the underlying distribution has been widely debated. This paper presents the generalized bicubic distribution as a good alternative to the beta distribution since, when the variance depends on the mode, the generalized bicubic distribution approximates the kurtosis of the Gaussian distribution better than the beta distribution. In addition, this distribution presents good properties in the PERT methodology in relation to moderation and conservatism criteria. Two empirical applications are presented to demonstrate the adequateness of this new distribution. López Martín, María del Mar verfasserin (DE-588)1158691211 (DE-627)1020751452 (DE-576)504303198 aut García García, Catalina verfasserin aut Sánchez Granero, Miguel Ángel verfasserin aut Enthalten in Operations research perspectives Amsterdam [u.a.] : Elsevier, 2014 3(2016), Seite 67-76 Online-Ressource (DE-627)826105165 (DE-600)2821932-6 (DE-576)433076496 2214-7160 nnns volume:3 year:2016 pages:67-76 http://hdl.handle.net/10419/178270 Resolving-System kostenfrei Volltext https://doi.org/10.1016/j.orp.2016.09.001 Resolving-System kostenfrei Volltext https://www.sciencedirect.com/science/article/pii/S2214716016300252/pdfft?md5=baa3bd0ff53f1d679cd845cf40fb3ea5&pid=1-s2.0-S2214716016300252-main.pdf Verlag kostenfrei Volltext http://creativecommons.org/licenses/by-nc-nd/4.0/ Verlag Terms of use 46 GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 3 2016 67-76 26 01 0206 1767049218 x1k 25-04-18 26 00 DE-206 56 Uncertainty 26 00 DE-206 56 PERT 26 00 DE-206 56 Bicubic distribution 26 00 DE-206 56 Beta distribution
allfieldsGer	10.1016/j.orp.2016.09.001 doi 10419/178270 hdl (DE-627)1019501820 (DE-599)GBV1019501820 DE-627 ger DE-627 rda eng García Peréz, José Ignacio verfasserin (DE-588)171340957 (DE-627)06153174X (DE-576)132142252 aut Project management under uncertainty beyond beta the generalized bicubic distribution José García Pérez, María del Mar López Martín, Catalina García García, Miguel Ángel Sánchez Granero 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The beta distribution has traditionally been employed in the PERT methodology and generally used for modeling bounded continuous random variables based on expert’s judgment. The impossibility of estimating four parameters from the three values provided by the expert when the beta distribution is assumed to be the underlying distribution has been widely debated. This paper presents the generalized bicubic distribution as a good alternative to the beta distribution since, when the variance depends on the mode, the generalized bicubic distribution approximates the kurtosis of the Gaussian distribution better than the beta distribution. In addition, this distribution presents good properties in the PERT methodology in relation to moderation and conservatism criteria. Two empirical applications are presented to demonstrate the adequateness of this new distribution. López Martín, María del Mar verfasserin (DE-588)1158691211 (DE-627)1020751452 (DE-576)504303198 aut García García, Catalina verfasserin aut Sánchez Granero, Miguel Ángel verfasserin aut Enthalten in Operations research perspectives Amsterdam [u.a.] : Elsevier, 2014 3(2016), Seite 67-76 Online-Ressource (DE-627)826105165 (DE-600)2821932-6 (DE-576)433076496 2214-7160 nnns volume:3 year:2016 pages:67-76 http://hdl.handle.net/10419/178270 Resolving-System kostenfrei Volltext https://doi.org/10.1016/j.orp.2016.09.001 Resolving-System kostenfrei Volltext https://www.sciencedirect.com/science/article/pii/S2214716016300252/pdfft?md5=baa3bd0ff53f1d679cd845cf40fb3ea5&pid=1-s2.0-S2214716016300252-main.pdf Verlag kostenfrei Volltext http://creativecommons.org/licenses/by-nc-nd/4.0/ Verlag Terms of use 46 GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 3 2016 67-76 26 01 0206 1767049218 x1k 25-04-18 26 00 DE-206 56 Uncertainty 26 00 DE-206 56 PERT 26 00 DE-206 56 Bicubic distribution 26 00 DE-206 56 Beta distribution
allfieldsSound	10.1016/j.orp.2016.09.001 doi 10419/178270 hdl (DE-627)1019501820 (DE-599)GBV1019501820 DE-627 ger DE-627 rda eng García Peréz, José Ignacio verfasserin (DE-588)171340957 (DE-627)06153174X (DE-576)132142252 aut Project management under uncertainty beyond beta the generalized bicubic distribution José García Pérez, María del Mar López Martín, Catalina García García, Miguel Ángel Sánchez Granero 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The beta distribution has traditionally been employed in the PERT methodology and generally used for modeling bounded continuous random variables based on expert’s judgment. The impossibility of estimating four parameters from the three values provided by the expert when the beta distribution is assumed to be the underlying distribution has been widely debated. This paper presents the generalized bicubic distribution as a good alternative to the beta distribution since, when the variance depends on the mode, the generalized bicubic distribution approximates the kurtosis of the Gaussian distribution better than the beta distribution. In addition, this distribution presents good properties in the PERT methodology in relation to moderation and conservatism criteria. Two empirical applications are presented to demonstrate the adequateness of this new distribution. López Martín, María del Mar verfasserin (DE-588)1158691211 (DE-627)1020751452 (DE-576)504303198 aut García García, Catalina verfasserin aut Sánchez Granero, Miguel Ángel verfasserin aut Enthalten in Operations research perspectives Amsterdam [u.a.] : Elsevier, 2014 3(2016), Seite 67-76 Online-Ressource (DE-627)826105165 (DE-600)2821932-6 (DE-576)433076496 2214-7160 nnns volume:3 year:2016 pages:67-76 http://hdl.handle.net/10419/178270 Resolving-System kostenfrei Volltext https://doi.org/10.1016/j.orp.2016.09.001 Resolving-System kostenfrei Volltext https://www.sciencedirect.com/science/article/pii/S2214716016300252/pdfft?md5=baa3bd0ff53f1d679cd845cf40fb3ea5&pid=1-s2.0-S2214716016300252-main.pdf Verlag kostenfrei Volltext http://creativecommons.org/licenses/by-nc-nd/4.0/ Verlag Terms of use 46 GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 3 2016 67-76 26 01 0206 1767049218 x1k 25-04-18 26 00 DE-206 56 Uncertainty 26 00 DE-206 56 PERT 26 00 DE-206 56 Bicubic distribution 26 00 DE-206 56 Beta distribution
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source	Enthalten in Operations research perspectives 3(2016), Seite 67-76 volume:3 year:2016 pages:67-76
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authorswithroles_txt_mv	García Peréz, José Ignacio @@aut@@ López Martín, María del Mar @@aut@@ García García, Catalina @@aut@@ Sánchez Granero, Miguel Ángel @@aut@@
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author	García Peréz, José Ignacio
spellingShingle	García Peréz, José Ignacio 26 Uncertainty 26 PERT 26 Bicubic distribution 26 Beta distribution Project management under uncertainty beyond beta the generalized bicubic distribution
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topic_title	26 00 DE-206 56 Uncertainty 26 00 DE-206 56 PERT 26 00 DE-206 56 Bicubic distribution 26 00 DE-206 56 Beta distribution Project management under uncertainty beyond beta the generalized bicubic distribution José García Pérez, María del Mar López Martín, Catalina García García, Miguel Ángel Sánchez Granero
topic	26 Uncertainty 26 PERT 26 Bicubic distribution 26 Beta distribution
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title	Project management under uncertainty beyond beta the generalized bicubic distribution
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title_full	Project management under uncertainty beyond beta the generalized bicubic distribution José García Pérez, María del Mar López Martín, Catalina García García, Miguel Ángel Sánchez Granero
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author-letter	García Peréz, José Ignacio
title_sub	the generalized bicubic distribution
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title_sort	project management under uncertainty beyond betathe generalized bicubic distribution
title_auth	Project management under uncertainty beyond beta the generalized bicubic distribution
abstract	The beta distribution has traditionally been employed in the PERT methodology and generally used for modeling bounded continuous random variables based on expert’s judgment. The impossibility of estimating four parameters from the three values provided by the expert when the beta distribution is assumed to be the underlying distribution has been widely debated. This paper presents the generalized bicubic distribution as a good alternative to the beta distribution since, when the variance depends on the mode, the generalized bicubic distribution approximates the kurtosis of the Gaussian distribution better than the beta distribution. In addition, this distribution presents good properties in the PERT methodology in relation to moderation and conservatism criteria. Two empirical applications are presented to demonstrate the adequateness of this new distribution.
abstractGer	The beta distribution has traditionally been employed in the PERT methodology and generally used for modeling bounded continuous random variables based on expert’s judgment. The impossibility of estimating four parameters from the three values provided by the expert when the beta distribution is assumed to be the underlying distribution has been widely debated. This paper presents the generalized bicubic distribution as a good alternative to the beta distribution since, when the variance depends on the mode, the generalized bicubic distribution approximates the kurtosis of the Gaussian distribution better than the beta distribution. In addition, this distribution presents good properties in the PERT methodology in relation to moderation and conservatism criteria. Two empirical applications are presented to demonstrate the adequateness of this new distribution.
abstract_unstemmed	The beta distribution has traditionally been employed in the PERT methodology and generally used for modeling bounded continuous random variables based on expert’s judgment. The impossibility of estimating four parameters from the three values provided by the expert when the beta distribution is assumed to be the underlying distribution has been widely debated. This paper presents the generalized bicubic distribution as a good alternative to the beta distribution since, when the variance depends on the mode, the generalized bicubic distribution approximates the kurtosis of the Gaussian distribution better than the beta distribution. In addition, this distribution presents good properties in the PERT methodology in relation to moderation and conservatism criteria. Two empirical applications are presented to demonstrate the adequateness of this new distribution.
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title_short	Project management under uncertainty beyond beta
url	http://hdl.handle.net/10419/178270 https://doi.org/10.1016/j.orp.2016.09.001 https://www.sciencedirect.com/science/article/pii/S2214716016300252/pdfft?md5=baa3bd0ff53f1d679cd845cf40fb3ea5&pid=1-s2.0-S2214716016300252-main.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/
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up_date	2024-07-04T20:12:58.124Z
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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">1019501820</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20210903191810.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180425s2016 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.orp.2016.09.001</subfield><subfield code="2">doi</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10419/178270</subfield><subfield code="2">hdl</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1019501820</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBV1019501820</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="100" ind1="1" ind2=" "><subfield code="a">García Peréz, José Ignacio</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)171340957</subfield><subfield code="0">(DE-627)06153174X</subfield><subfield code="0">(DE-576)132142252</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Project management under uncertainty beyond beta</subfield><subfield code="b">the generalized bicubic distribution</subfield><subfield code="c">José García Pérez, María del Mar López Martín, Catalina García García, Miguel Ángel Sánchez Granero</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</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">The beta distribution has traditionally been employed in the PERT methodology and generally used for modeling bounded continuous random variables based on expert’s judgment. The impossibility of estimating four parameters from the three values provided by the expert when the beta distribution is assumed to be the underlying distribution has been widely debated. This paper presents the generalized bicubic distribution as a good alternative to the beta distribution since, when the variance depends on the mode, the generalized bicubic distribution approximates the kurtosis of the Gaussian distribution better than the beta distribution. In addition, this distribution presents good properties in the PERT methodology in relation to moderation and conservatism criteria. 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Project management under uncertainty beyond beta : the generalized bicubic distribution

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