invGauss

invGauss: Threshold regression that fits the (randomized drift) inverse Gaussian distribution to survival data. invGauss fits the (randomized drift) inverse Gaussian distribution to survival data. The model is described in Aalen OO, Borgan O, Gjessing HK. Survival and Event History Analysis. A Process Point of View. Springer, 2008. It is based on describing time to event as the barrier hitting time of a Wiener process, where drift towards the barrier has been randomized with a Gaussian distribution. The model allows covariates to influence starting values of the Wiener process and/or average drift towards a barrier, with a user-defined choice of link functions.


References in zbMATH (referenced in 96 articles , 1 standard article )

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  1. Deresa, Negera Wakgari; Van Keilegom, Ingrid: A multivariate normal regression model for survival data subject to different types of dependent censoring (2020)
  2. Bednarski, Tadeusz; Skolimowska-Kulig, Magdalena: On scale Fisher consistency of maximum likelihood estimator for the exponential regression model under arbitrary frailty (2019)
  3. Bluhmki, Tobias; Dobler, Dennis; Beyersmann, Jan; Pauly, Markus: The wild bootstrap for multivariate Nelson-Aalen estimators (2019)
  4. Commenges, Daniel: Dealing with death when studying disease or physiological marker: the stochastic system approach to causality (2019)
  5. Furrer, Christian: Experience rating in the classic Markov chain life insurance setting (2019)
  6. Golzy, Mojgan; Carter, Randy L.: Generalized frailty models for analysis of recurrent events (2019)
  7. Jullum, Martin; Hjort, Nils Lid: What price semiparametric Cox regression? (2019)
  8. Karev, Georgy P.; Novozhilov, Artem S.: How trait distributions evolve in populations with parametric heterogeneity (2019)
  9. van den Hout, Ardo; Muniz-Terrera, Graciela: Hidden three-state survival model for bivariate longitudinal count data (2019)
  10. Yamakou, Marius E.; Tran, Tat Dat; Duc, Luu Hoang; Jost, Jürgen: The stochastic FitzHugh-Nagumo neuron model in the excitable regime embeds a leaky integrate-and-fire model (2019)
  11. Djeundje, Viani Biatat; Crook, Jonathan: Incorporating heterogeneity and macroeconomic variables into multi-state delinquency models for credit cards (2018)
  12. Leão, Jeremias; Leiva, Víctor; Saulo, Helton; Tomazella, Vera: A survival model with Birnbaum-Saunders frailty for uncensored and censored cancer data (2018)
  13. Liu, Wanrong; Fang, Jianglin; Lu, Xuewen: Additive-multiplicative hazards model with current status data (2018)
  14. Riva Palacio, Alan; Leisen, Fabrizio: Bayesian nonparametric estimation of survival functions with multiple-samples information (2018)
  15. Ytterstad, Elinor: Frailty in survival analysis of widowhood mortality (2018)
  16. Zarezade, Ali; De, Abir; Upadhyay, Utkarsh; Rabiee, Hamid R.; Gomez-Rodriguez, Manuel: Steering social activity: a stochastic optimal control point of view (2018)
  17. Ghebremichael-Weldeselassie, Yonas; Whitaker, Heather J.; Douglas, Ian J.; Smeeth, Liam; Farrington, C. Paddy: Self-controlled case series with multiple event types (2017)
  18. Jiang, Fei; Haneuse, Sebastien: A semi-parametric transformation frailty model for semi-competing risks survival data (2017)
  19. Kayid, M.; Izadkhah, S.; Zuo, Ming J.: Some results on the relative ordering of two frailty models (2017)
  20. Leão, Jeremias; Leiva, Víctor; Saulo, Helton; Tomazella, Vera: Birnbaum-Saunders frailty regression models: diagnostics and application to medical data (2017)

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