The survival regression model of competing risks for the family of Farlie–Gumbel–Morgenstern distributions
In this paper the trivariate survival regression model for FGM family of distributions is constructed with marginal left-truncated logistic distributions. Two methods (using survival and hazard functions in the first case, and distributional density and ` `conditional'' survival function...
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| Format: | Article |
| Language: | English |
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Vilnius University Press
2002-12-01
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| Series: | Lietuvos Matematikos Rinkinys |
| Online Access: | https://www.zurnalai.vu.lt/LMR/article/view/32987 |
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| _version_ | 1825197238801924096 |
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| author | Audronė Jakaitienė Danas Zuokas |
| author_facet | Audronė Jakaitienė Danas Zuokas |
| author_sort | Audronė Jakaitienė |
| collection | DOAJ |
| description |
In this paper the trivariate survival regression model for FGM family of distributions is constructed with marginal left-truncated logistic distributions. Two methods (using survival and hazard functions in the first case, and distributional density and ` `conditional'' survival function in the second case) are used when constructing likelihood function for model parameter estimation. Constructed survival model was run with the data of the ` `KRIS'' (The Kaunas Rotterdam Intervention Study), which lasted for 22 years from 1972. The results show, that using second case for likehhood function construction gives better approximation for the data, because some additional conditions are considered.
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| format | Article |
| id | doaj-art-56db3b1fc0864f5a9032c40d8619bd7c |
| institution | Kabale University |
| issn | 0132-2818 2335-898X |
| language | English |
| publishDate | 2002-12-01 |
| publisher | Vilnius University Press |
| record_format | Article |
| series | Lietuvos Matematikos Rinkinys |
| spelling | doaj-art-56db3b1fc0864f5a9032c40d8619bd7c2025-02-11T18:13:18ZengVilnius University PressLietuvos Matematikos Rinkinys0132-28182335-898X2002-12-0142spec.10.15388/LMR.2002.32987The survival regression model of competing risks for the family of Farlie–Gumbel–Morgenstern distributionsAudronė Jakaitienė0Danas Zuokas1Vytautas Magnus UniversityVytautas Magnus University In this paper the trivariate survival regression model for FGM family of distributions is constructed with marginal left-truncated logistic distributions. Two methods (using survival and hazard functions in the first case, and distributional density and ` `conditional'' survival function in the second case) are used when constructing likelihood function for model parameter estimation. Constructed survival model was run with the data of the ` `KRIS'' (The Kaunas Rotterdam Intervention Study), which lasted for 22 years from 1972. The results show, that using second case for likehhood function construction gives better approximation for the data, because some additional conditions are considered. https://www.zurnalai.vu.lt/LMR/article/view/32987 |
| spellingShingle | Audronė Jakaitienė Danas Zuokas The survival regression model of competing risks for the family of Farlie–Gumbel–Morgenstern distributions Lietuvos Matematikos Rinkinys |
| title | The survival regression model of competing risks for the family of Farlie–Gumbel–Morgenstern distributions |
| title_full | The survival regression model of competing risks for the family of Farlie–Gumbel–Morgenstern distributions |
| title_fullStr | The survival regression model of competing risks for the family of Farlie–Gumbel–Morgenstern distributions |
| title_full_unstemmed | The survival regression model of competing risks for the family of Farlie–Gumbel–Morgenstern distributions |
| title_short | The survival regression model of competing risks for the family of Farlie–Gumbel–Morgenstern distributions |
| title_sort | survival regression model of competing risks for the family of farlie gumbel morgenstern distributions |
| url | https://www.zurnalai.vu.lt/LMR/article/view/32987 |
| work_keys_str_mv | AT audronejakaitiene thesurvivalregressionmodelofcompetingrisksforthefamilyoffarliegumbelmorgensterndistributions AT danaszuokas thesurvivalregressionmodelofcompetingrisksforthefamilyoffarliegumbelmorgensterndistributions AT audronejakaitiene survivalregressionmodelofcompetingrisksforthefamilyoffarliegumbelmorgensterndistributions AT danaszuokas survivalregressionmodelofcompetingrisksforthefamilyoffarliegumbelmorgensterndistributions |